NSF ERC for Future Cities:

Data-Enabled Resiliency and Sustainability

By

Columbia University

City College of New York

University of California, Riverside

University of Notre Dame

Project Summary

(1 page): Project Summaries must include three titled sections: Overview statement on Vision, Intellectual Merit, and Statement on Broader Impacts. The summary should be written in the third person, informative to those working in the same or related field(s), and understandable to a scientifically or technically literate reader. Preliminary proposals that do not contain the Project Summary, including an overview and separate statements on intellectual merit and broader impacts will not be accepted by FastLane or will be returned without review. Additional instructions for preparation of the Project Summary are available in FastLane.Delete this box after reading

1

Our aging, overburdened urban infrastructure is increasingly vulnerable to deterioration and extreme events. The $72 billion in damage from the 2012 super storm Sandy to the New York City metropolitan region has highlighted the significant challenge to traditional approaches for managing increasingly complex urban systems and for responding to more frequent weather-related hazards.

VISION: Empowered by advances in Big Data and encouraged by citizen enthusiasm for social-media reporting in recent disasters, the proposed ERC for Future Cities will pioneer a revolutionary sensor network involving ubiquitous citizen sensors to extract and deliver meaningful engineering decisions for intelligent management of urban infrastructure under normal and extreme events. Sensor data, flowing in real time and processed by advanced analytics, offers a unique, and as yet untapped, means to deploy a new paradigm to measure and understand vulnerabilities, anticipate failures, cost-effectively manage assets, and design adaptive strategies, thus transforming America’s urban infrastructure into a smart, safe, resilient, and sustainable foundation of economic prosperity and quality of life.

INTELLECTUAL MERIT: The proposed ubiquitous sensor network, which takes advantage of human senses and smartphone sensors integrated with ad hoc low-cost sensors and remote sensing, has never been explored for monitoring the integrity of urban infrastructure and its natural environment. The idea of allowing citizens to be stewards of their infrastructure and cities is groundbreaking despite concerns of privacy. The real intellectual challenges, however, lie in how to extract actionable knowledge from what is intrinsically distributed, noisy, heterogeneous and uncalibrated data and, more importantly, how to deliver that knowledge to increase acceptance by end users in order to achieve the envisioned data-enabled resiliency and sustainability. To overcome these challenges, the ERC will conduct fundamental science and engineering research to create a robust ubiquitous sensor network for intelligent monitoring and new data-enabled, visualized analytics, assembling a team of researchers with vast experience in all aspects of urban systems sensing/monitoring/analysis, cyberinfrastructure and crowdsourcing for hazard applications, and urban policy. The research deploys pilot projects in three test-beds, organized into four thrust areas. (1) Ubiquitous Sensors - novel and optimal uses of citizen sensors and development of needed low-cost infrastructure sensors; (2) Data Processing – methods for distributed crowdsourced data processing and heterogeneous data fusion; (3) Data-Enabled Analytics – frameworks for diagnostics of structural health, modeling of material/structural deterioration, and assessment of infrastructure fragility to weather hazards considering deterioration, and (4) Integrated Systems – Test-bed proof-of-concept and demonstration of the real-time Natural/Built Environment Spatio-Temporal (INBEST) Information and Database System tools for supporting (a) scientific prioritization of maintenance/repair based on data-identified structural vulnerability and importance to the system; (b) resiliency design strategies taking into consideration of life-cycle costs and socioeconomic impacts; and (c) effective emergency response.

BROADER IMPACTS: The ERC research will deliver fundamental knowledge, enabling technologies, and proof-of-concept systems for a new paradigm of intelligent asset management and informed adaptive planning, thus reducing loss to hazards and increasing sustainability by minimizing life-cycle costs. Workforce development impacts will result from technology adoption, seeded at multiple stages of the educational pipeline, integrated with curriculum development particularly benefitting underrepresented minorities. Engagement to the citizen sensor research at the pre-college education level, including crowdsourcing competitions and games on social media, will be geared to inspire next-generation STEM professionals and empower general public infrastructure advocates to participate. Columbia’s Harlem Consortium will offer wide message propagation with refinements over time to sustain student interest and inform decisions about STEM careers. New undergraduate and graduate programs in Digital Urban Engineering - fusing engineering, science, policy and urban planning - will seed intermediate educational stages via cyber-based experiential learning modules and contextual test-bed projects. Cyberinfrastucture scaling, extending engagement to project teams at partner institutions, will demonstrate a capacity for distributed analysis and off-site decision support. To extend the ERC’s reach, open-source educator toolkits with ABET-compliant modules will be made available nationally to relevant programs. The ERC will enable partnerships with end users and industry and use of New York City – reliant on a large, aging,

2

and complex infrastructure exposed to a spectrum of hazards – as test-beds for rapid, seamless translation of the ERC for Future Cities products to realize the proposed vision.

Project Description: Maximum 7 pages total, containing the following sections:1. Proposing Team: Start the description with a table that that has four columns: (1) Name of the PI or co-PIs, (2) Institution, (3) Department(s), and (4) Most Relevant Field(s) of Expertise. There will be up to five rows, one for the PI (Center Director) and one each for up to four co-PIs.

Name Institute Department Field of ExpertiseMaria Q. Feng, PI Columbia

UniversityCivil Engrg. & Engrg. Mechanics, SEAS;

Data Institute for Sci. & Engr., Smart Cities

Advanced sensors, structural health monitoring and damage diagnostics, bridge & building model updating and fragility analysis, intelligent inspection

George Deodatis, Co-PI

Columbia University

Civil Engrg. & Engrg. Mechanics, SEAS

Multi-hazards, risk & resiliency assessment, mitigation strategies and adaptive planning, transportation infrastructure

Ester R. Fuchs, Co-PI

Columbia University

Urban Policy Program, Intl. & Public Affairs

Urban policy & sustainability, urban workforce development, Advisor to the Mayor of NYC

Eamonn Keogh, Co-PI

University of California, Riverside

Computer Science Data mining of sensor data, applied signal processing, reasoning under uncertainty, crowdsourcing

Tracy Kijewski-Correa, Co-PI

Notre Dame University

Civil & Envirn. Engrg. & Earth Sciences

Structural health monitoring, cyberinfrastructure/crowdsourcing/citizen engineering for integrated civil infrastructure research, education & outreach

2. Ten-Year Vision for a transformational engineered system and its potential impact on society. Justification of why a center is needed to achieve that vision as opposed to a number of single investigator projects or small group grants.

Having experienced rapid urbanization over the last century, the U.S., along with many other countries, is now facing the formidable challenge of managing its aging and deteriorating urban infrastructure with limited fiscal resources. Increasing population further stresses the overburdened urban support systems, making them increasingly vulnerable to extreme events, threatening the quality of life and economic prosperity. The complexity of interacting systems of the built, human, and natural environment, coupled with increasing risk of extreme weather events, pose significant challenges for traditional engineering approaches to cost-effectively manage the aging systems. The $72 billion in damage from the 2012 super storm Sandy to the New York City metropolitan region highlighted the urgent need to address the urban infrastructure crisis. At the same time, this event also demonstrated the emergence, albeit ad hoc, of a massive data stream from social media and personal sensors. Encouraged by citizens’ enthusiasm for social-media reporting in recent disasters and empowered by advances in Big Data, the proposed ERC for Future Cities will pioneer a revolutionary sensor network involving citizen sensors to extract and deliver meaningful engineering decisions from the massive inputs of disparate sensors, for intelligent management of urban built environment under normal and extreme events. Sensor data, flowing in real time and processed by advanced analytics, offers a unique, and as yet untapped, means to deploy a new paradigm to measure and understand vulnerabilities, anticipate failures, and proactively manage assets, thus transforming America’s urban infrastructure into a smart, safe, resilient, and sustainable foundation of economic prosperity and quality of life

3

To achieve this vision of data-enabled resiliency and sustainability, the ERC will investigate and deploy means for a ubiquitous sensor network involving distributed citizen sensors to intelligently monitor urban infrastructure in order to (1) gain a more complete understanding of infrastructure vulnerabilities through enhanced information and analysis frameworks; (2) manage the aging infrastructure and its use more cost-effectively; and (3) design systems adaptive to identified weaknesses and potential extreme events and to exploit technological opportunities to enhance system performance.The breadth and depth of the challenges require a ERC structure to synergistically integrate the needed spectrum of expertise in engineering, natural science, and social science to pursue a research agenda in smartphone-based citizen sensors, low-power low-cost sensors, distributed data processing under uncertainties, data privacy and security, gametification and citizen engagement, and advanced visual analytics. The Center model is especially critical for successful engagement of citizens to serve as sensors and infrastructure owners to support the important test-bed validation of the data-enabled analytics for structural health diagnostics and infrastructure system fragility assessment. A new era in smart urban infrastructure also requires the creation of a technology-empowered workforce and the Center model is needed to systematically seeds workforce development at multiple stages of the educational pipeline. The proposed citizen sensor network offers a unique opportunity to engage broad participation from the general public, K-12-Gray, to smart urban infrastructure research and education, , which requires a high-level organization. The ERC’s multidisciplinary expertise is a pre-requisite for the systematic creation a new Digital Urban Engineering discipline for undergraduate and graduate studies. Furthermore, the NSF ERC’s scope, longevity and visibility will enable substantial partnerships with urban infrastructure owners, such as NYC Department of Transportation (DOT) and Metropolitan Transportation Agency (MTA) to transform research into actionable knowledge and tools. For similar reasons, the ERC will attract wide participation of industry, produce new sensor and network products, provide next-generation network and data services, and implement the intelligent maintenance and adaptive design of urban infrastructure. The cutting-edge research and partnerships with end users and industry will make the ERC an ideal ecosystem to nurture pioneering ideas into technology innovations and startups.

3. Strategic Research Plan must clearly identify the fundamental insights that will enable the proposed ERC to achieve its vision. Strategic Research Plan must include a graphical depiction using the ERC Program's 3-Plane Strategic Planning Chart. A sample chart can be found on the ERC Association's Website http://erc-assoc.org/content/templates-proposal-preparation-0. The chart tailored to the proposed ERC research plan must be placed in this section and it must be on a scale and in a font size that are readable.

For the proposed ubiquitous sensing, significant technical barriers lie in how to extract actionable knowledge from what is intrinsically distributed noisy, heterogeneous and uncalibrated data and, more importantly, how to deliver that knowledge to increase acceptance by end users in order to achieve the envisioned data-enabled resiliency and sustainability. The research plan has been devised to overcome technical barriers, intellectually challenging and impervious to individual disciplinary effort, to achieve the proposed vision. As illustrated in Fig. 1, research will be organized in four key thrust areas and supported by three test-beds, building a knowledge base, developing enabling technologies, and finally delivering information systems to support intelligent management decisions toward smart, resilient, and sustainable urban infrastructure: (Thrust 1) Ubiquitous Sensors - novel and optimal uses of citizen sensors and development of needed low-cost ad hoc sensors; (Thrust 2) Data Processing – methods for distributed crowdsourced data processing and heterogeneous data fusion; (Thrust 3) Data-Driven Analytics – frameworks for diagnostics of structural health, modeling of material aging, estimating extreme weather hazards, and assessment of fragility and life-cycle cost, and (Thrust 4) Integrated Systems –INBEST Information and Database System tools for supporting (a) scientific prioritization of maintenance/repair based on identified structure health and relative importance; (b) adaptive design taking into account life-cycle costs and socioeconomic impacts; and (c) effective response to extreme events. Each of the INBEST tools will be validated in the test-bed pilot projects to demonstrate the data-enabled resiliency and sustainability of urban infrastructure.

4

To address the proposed inherently interdisciplinary problems, a close ERC collaborative research partnership has been established among civil, electrical and environmental engineers, computer, climate and social scientists, urban planners, economists, and policy experts from four institutions including two minority serving institutions. These highly qualified individuals will bring their much needed, complementary expertise and rich experience to the ERC.

Fig. 1 Strategic Research Plan

4. Research Plan to address the barriers to achieving the vision, including fundamentals through to proof-of-concept in enabling and

systems test beds.

The ERC research is organized in four research thrust areas with pilot projects supported by three test-beds.

Research Thrust I: Ubiquitous Sensors

Goal: An innovative ubiquitous sensor system for intelligent monitoring of urban infrastructureBarrier: Uncontrollable locations of citizen sensors, insufficient sensor performance Approach: Experimental study

The high cost of sensors, their installation and maintenance is a bottleneck to the nearly unlimited potential of the emerging big data revolution. Despite intensive research activity, there has been only limited adoption of structural health monitoring systems in civil infrastructure (Catbas and Kijewski-Correa, 2013). Particularly challenging, due to costly cabling and the limited durability of conventional

5

fixed electrical sensors, is monitoring spatially distributed civil infrastructures exposed to harsh environments. To overcome this barrier, the ERC will pioneer an innovative ubiquitous sensor network -- including citizen sensors -- to take advantage of human senses as well as sensors embedded in or linked to their smartphones, tablets, PCs, and cars to broadly and continuously collect and mine an unprecedented wealth of multi-media data at low cost. This would extend the past work by the project team in crowdsourced self-reporting of qualitative infrastructure condition using geotagged smartphone images (Kijewski-Correa, 2011) by extracting quantitative data. Through the work of the PI, such potentials have already been demonstrated: Figure 2 shows two tests carried out by the PI; one is to fix a smartphone on a shaker to measure 1 Hz - 10 Hz sinusoidal acceleration, and the other is to remotely measure the shaker’s sinusoidal displacement by tracking a marker on the shaker (Fukuda, et al, 2010). The results are highly encouraging. Such vibration measurements are a valuable data source to validate in-situ performance of infrastructure systems for decision support, including the diagnosis of deterioration and damage (Catbas, et al. 2013; Feng, 2009). The citizen sensors, however, does suffer from significant drawbacks such as uncontrollable sensor locations and low sensor performance. For example, the current accelerometers in smartphones lack sufficient sensitivity for measuring low-frequency vibration experienced by long-span bridges and highrise buildings. This research will investigate the feasibilities of using smartphone’s (1) accelerometers at uncontrolled locations to measure structural vibration, (2) cameras to detect visual change in structure surface (such as cracks), and (3) cameras to measure dynamic displacement of structures by real-time processing of video images. Experiments will be carried out on a new office building on Columbia campus to compare measurements by mobile smartphone sensors and fixed high-end sensors. The most ideal sensor layout, and the best usage of the smartphone sensors, in terms of the type, number, and location of sensors, will be identified. For essential structures, permanently-installed high-end, low-cost ad hoc sensors are needed to supplement the citizen sensors. This research will develop low-cost, power-harvesting, low-maintenance micro sensors (such as MEMS corrosion sensors) and sensor networks, building upon the team’s extensive experience in this area (e.g., Feng, 1994; Feng, 1996; Feng 1998; Feng and Kim, 2006). These sensors can also be connected externally to smartphones to transmit signals on mobile phone network or Internet and will incorporate the team’s research on network architectures and hardware design to support deployment by non-expert citizens (Kijewski-Correa et al, 2012).

Figure 2 Smartphone Sensor Measurements

Research Thrust 2: Distributed Crowdsourced Data Processing a) To extract useful information from noisy, heterogeneous, uncalibrated data;

0 2500 50009.67

9.77

9.87

Measured acceleration at 1Hz

Time, ms

Acc

el.,

m/s2

0 250 5000

10

20 Measured acceleration at 10Hz

Time, ms

Acc

el.,

m/s2

0 250 5000

10

20 Measured acceleration at 10Hz

Time, ms

Acc

el.,

m/s2

0 250 500

0

1

2Measured displacement at 10 Hz

Time, ms

Disp

l., m

m

6

b) Fusion of multi-media sensor data with geographic information system (GIS) c) Privacy-preserving service d) Security-aware crowdsourcing

Research Thrust 2: Distributed Crowdsourced Data Processing Crowdsourcing is a distributed problem solving paradigm in which a population of undefined size solves a problem for a monetary or intangible (i.e. bragging rights, intellectual curiosity) benefit through an open call. In addition to commercial crowdsourcing (i.e. reCAPTCHA.net), there are dozens of academic and non-profit projects such as Galaxy Zoo and Foldit which allow the public solve scientific problems by playing games (Saxton et. al. 2013). All these crowdsourcing frameworks are inherently participatory, since they require the active participation of users. Recently, the widespread availability of smartphone devices featuring sensing capabilities are providing the means for opportunistic crowdsourcing frameworks; these are passive and require little or no user intervention. Examples include Ear-Phone (Rana et. al. 2011) which enables the construction of detailed noise maps and PotHole (Eriksson et. al 2008), which allows smartphone users to share their vibration and location data in order to allow road condition assessment.Our proposed framework falls somewhere between participatory and opportunistic. We plan to leverage off the wealth of high quality sensors available in the now ubiquitous smartphones, however we can also leverage some element of participation by users in annotating data and placing smartphones at the most useful locations. There are already some precedents for this. For example, WAZE.com, a social GPS application gathers implicitly extracted information about traffic combined with opportunities for participants to actively annotate data with images/text (Thiagarajan et. al 2009). Below we give some example scenarios of the projects we plan to investigate: After a major storm hits upstate New York. Miguel, a volunteer citizen scientist visits a bridge near his

house to inspect it for damage. As shown in Fig 3, he does discover and photograph a crack, but is it a result of the recent storm? By uploading the image to a central server, it can be automatically aligned and compared with previous images of the same truss, even if the other images were taken from different angles, with different cameras, under different light conditions, etc. The database of existing images includes images taken by Miguel’s nine-year-old niece, as part of our K12 STEM outreach.

Fig 3. An example scenario: left) After a major storm a volunteer citizen scientist visits and inspects a bridge. center) He notices and photographs a crack in a support truss, but is it the result of the recent storm? right) Using a tool built on top of Microsoft’s photosynth engine (Karl et. al. 2010), we can see that the crack is indeed new.

Planning evacuation routes, or routes to avoid damage infrastructure is a difficult task as there is typically few precedents of similar-size disasters to draw from. Most attempts rely on simulations. However, we can exploit a wealth of history of more innocuous congestion events (sporting events, concerts etc) to opportunity gather data which can either prime the simulations, or simply replace them (Halevy et. al 2009) . Based on analysis of this historical data, traffic planners will know where to place signage and/or traffic officers to redirect cars in case of a similar outage.

To enable these scenarios we will need to: Develop a gamification framework to incentivize participation (Zhu and Keogh 2010) (UCR/Notre Dame). Investigate distributed signal processing under the inevitably uncertain, noisy, and uncalibrated data we will encounter (Stajano et. al. 2010) (Columbia/UCR). Produce a framework that is resource (especially bandwidth) aware under both normal conditions and post disaster conditions (UCR). Address the inevitable data privacy and security issues that will arise (UCR/City).

7

Research Thrust 3: Data-Enabled AnalyticsGoal: To develop and validate advanced analytics for transforming sensor data into knowledge & actionBarrier: Model uncertainties Approach: Bayesian updating to reduce uncertainty using sensor data

Despite the extensive research over the last decade, there exists a significant gap between sensor data and their use for actions, which prevents implementation of sensor monitoring systems in actual civil infrastructure. To close this gap, the ERC will develop a number of data-enabled analytical tools; more specifically, frameworks will be developed for

(a) Model updating: Establish life-long database for essential bridges as well as buildings, which contains their analytical models with parameters, such as structural stiffness, identified from sensor measurements. These models are periodically updated by the continuously measured sensor data, serving as a baseline to identify post-event damage and monitoring structural aging. The project team brings to this proposal a unique living laboratory of long-term sensor data for a number of highway bridges (Feng et al, 2003; Gomez et al, 2011; ???) and tall buildings (Kijewski-Correa et al. 2003; Kijewski-Correa et al. 2006; Pirnia et al. 2007; Kijewski-Correa et al. 2013) that will now be expanded by the data measured by citizen sensors. This focus area will develop new data handling and structural identification techniques for this new data stream, capable of operating within the constraints and challenges associated with citizen deployed sensors (Kijewski-Correa et al. 2012). Selected algorithms will be developed into software packages, and even packaged as apps and tutorials, for test-bed bridges and buildings and be made available to limited public for trial use and improvement. In addition, material deterioration models will also be developed for steel and concrete structures and continuously updated based on monitoring data from corrosion and other sensors developed in Thrust 1.

(b) Assessment of network fragility and evaluation of resilient design strategies With the structural models (considering deterioration) continuously updated by sensor data, the vulnerability of the infrastructural system (consisting the component structures) to weather and other natural hazards can be more reliably assessed. This study will focus on the assessment of vulnerability of the NYC highway (including local streets) network to hurricane-induced winds and storm surges and sea-level rises exacerbated by global warming. GIS databases of the highway network (see Thrust 4) and fragility curves for bridge components and the network will be developed when subjected to such hazards. Using spatial-temporal probabilistic characteristics of hurricanes, expected losses can be calculated over different time horizons and over different spatial domains for the city. The team’s experience in seismic and wind fragility (Shinozuka, et al, 2000a; Shinozuka, et al, 2000b; Banerjee and Shinozuka, 2007; Siraki, et al, 2007; Zou, et al, 2010; Torbal, et al, 2012) will be useful for fragility analysis under storm surge, which has never been explored before. Beyond the assessment of vulnerability of the existing network in real time, this project will demonstrate the use of this framework for evaluating and comparing different resilient system design strategies. While this project focuses on highway transportation network, the approach can be applied to electric power grids and water transmission networks, for which the team also expertise (Shinozuka and Zhang, 2004, Shinozuka, et al, 2005). The ND team will also bring significant experience in fragility development for hurricanes, an important asset for the proposed effort, complement the Columbia team’s expertise in infrastructure networks.

Research Thrust 4: Integrated System and Test-BedsGoal: To develop an integrated, visualized, real-time information system and demonstrate its use as tools for decision support.

8

Barrier: Multiple data sources, large spatiotemporal data sets; Complex interactions among infrastructure systems and weather; Implementation obstacles

Approach: Novel data management, parallel hardware, visualization, end user engagement, novel policy mechanisms

Integrating research outcome in Thrusts 1, 2, and 3, the Natural/Built Environment Spatio-Temporal (INBEST) Information and Database System for NYC will be developed. The INBEST System represents highly advanced, visual geographical information that integrates real-time citizen sensor mapping with ad hoc fixed infrastructure sensors, overlaid with multiple data sources related to severe weather information and urban infrastructure. In addition, the real-time health conditions of the essential structures identified from the Thrust 3 framework will also be displayed. Socioeconomic data sets can also be queried. Figure 3 illustrates selected samples of weather and urban infrastructure related geospatial data. Since the spatial and temporal coverage of severe weather events provided by National Weather Service (NWS) is very coarse, the weather satellite imagery and the in-situ observation data can be used to refine the spatial and temporal coverage of the targeted event, through, for example, the Query-Driven Visual Exploration (QDVE) tool developed by one of the ERC researchers (Zhang et al. 2009; Zhang and You, 2010). The refined spatial and temporal coverages then will be used to query the NYC infrastructure datasets to derive statistics based on spatial analysis tools in GIS and/or spatial queries in spatial databases.

The CCNY team has been collecting and ingesting NYC urban infrastructure and their utilization data over the past few years, including Bytes of Big Apple from NYC Department of City Planning (DCP), Points of Interests (POIs) and 15-minute speed profile data from NAVTEQ/Nokia, subway turnstile data at 400+ subway stations at every 4- hours resolution from MTA NYC and on-time performance of domestic flights data from US Department of Transportation (DOT). The current MTA BusTime system generates nearly 2 million GPS records per day from thousands of GPS-equipped MTA NYC buses. The team is actively working with NYC DOT to access raw data from MTA directly in both real-time and batch modes. In addition, through collaboration with NYC Taxi and Limousine Commission (TLC), the CCNY team has access to un-sampled GPS taxi trip records since 2009. New types of sensor data from turnstiles, buses, taxies and flights, when properly aligned to urban transportation networks and critical infrastructures, can provide invaluable understanding of the interactions between causes (e.g., severe weather events, large-scale social activities, infrastructure malfunctions) and impacts (e.g., travel patterns,

Fig. 3. Understanding interactions between weather & urban infrastructure from multi-source data integration: (a) NWS Tornado Forecast (b) NOAA Satellite Imagery (c) NYCMetNet Wind Profiling (d) NEXRAD Reflectivity Data (e) MTA Subway Status Announcement (f) Map of NYC Bridges and Tunnels

9

traffic congestions and environmental consequences). To handle large–scale data and heavy computation, the team has been seeking a parallel approach to speed up Big Data management and analytics. The team has developed Graphics Processing Unit (GPU) –based parallel designs and implementations that scale well from single computing node to clusters. Experiments on zonal analysis of ~170 million taxi trip records over 40+ thousand census blocks have shown that the processing times can be reduced from hours to seconds on a single GPU device (Zhang et al 2012a, Zhang et al 2012b). The scalability of the implementations haves been tested on the world’s #1 supercomputer Titan located at the Oak Ridge National Laboratory (ORNL).

In a similar effort, the ND team has developed a risk assessment framework, all using cyberinfrastructure, that calculates the inundation due to waves and surge in a highly efficient manner based on ADCIRC-SWAN runs. This CyberEye platform includes a space where people can upload damage reports (damage database) and explore them in a viewer. A screen shot in Fig. 4 shows the data reported by citizens into platform from super storm Sandy.

Building upon the team’s rich experience, this Thrust will vigorously explore novel data management and analytics techniques on parallel hardware and develop end-to-end systems to facilitate actionable knowledge extraction and sensible decision making from large-scale, distributed and heterogeneous datasets. Three test-beds, the NYC Transportation Network, the Columbia University Campus, and the Chicago Highrise Buildings, will be developed, on which pilot projects will validate and demonstrate the usefulness of the INBEST system tools for (a) setting targets and priorities for cost-effective maintenance and repair of structures based on their current structural health conditions and relative importance in the system; (b) planning for inefficiency-adaptive, hazard-resilient designs by comparing different strategies taking into consideration of life-cycle costs and socioeconomic impacts; (c) supporting real-time emergency response operation and restoration. Novel policy mechanisms will also be explored to effectively motivate and engage the decision makers to participate the test-bed projects (Fuchs, 1992, 1996, 2012a). Ultimately, this research will prove the concept of the citizen-engaged ubiquitous sensor system and demonstrate the usefulness of the sensor data for more cost-effective management of infrastructure assets, designing more resilient infrastructure systems and emergency response, thus reducing human and economic loss to hazards and enhancing urban infrastructure sustainability in terms of minimizing life-cycle costs

5. Workforce Development (Education) Plan that includes innovations in pre-college through university education and research training.

While collaboratories of researchers in wide ranging fields have demonstrated that the collective expertise, resources and facilities made possible by center-level initiatives dramatically enhance the opportunities for discovery, their outcomes often fall short of their true potential and fail to translate to the constituents they were intended to serve by not recognizing the barriers that often isolate these collaboratories in academic silos. Three elements are central to enabling truly transformative and translational research that avoids these pitfalls: collective expertise, seamless integration using advanced technology, and effective reduction of barriers to knowledge for the purposes of workforce development and wider public advocacy for smart urban infrastructure. To do so, this ERC consciously chose ubiquitous sensing integrated by cyberinfrastructure, with an explicit involvement of the public at large as sensing elements – literally creating a technology-empowered workforce. This represents a natural synergy between workforce

Figure 4. Citizen Damage Reports on CyberEye from Super Storm Sandy

10

development and the research itself, where such development may be more fittingly described as technology adoption (Moore 2005) and innovative knowledge delivery, seeded at three stages of the educational pipeline to facilitate a paradigm shift in community engagement (Schneider 1983; West and O'Mahony 2008; Au et al. 2009) in public stewardship for infrastructure. The development activities at each of these three stages are now briefly described. Segment 1 -- Public Advocacy: The engagement of public at large in the stewardship of our infrastructure represents a significant effort at developing a citizen engineering workforce, who become inspired Infrastructure Advocates. The programs developed for workforce development in this segment will only be successful by synergizing with the ERC’s research thrusts and leveraging the personal networks of participants to achieve broad participation within the public. Due to the reliance on smartphones and other advanced technologies and greater likelihood of innovation among younger demographics, we will target two distinct groups: pre-college and Gen X/Y individuals, recruited using online campaigns and information kiosks at major events frequented by this demographic. Our position in New York City moreover allows exposure of the local population as well as tourist populations. All content will be delivered through SmartPhone apps users can freely download. Apps will deliver a variety of educational content and prompt for user responses and interaction to join the ERC in meeting its objectives through various tasks. The apps will each be designed for specific segments of each cohort, with content and packaging to maximize appeal to that age group, using gaming and social media recognition of status and share/referrals to increase participation and attract participants as well as measure success of knowledge delivery. Programming targeting the pre-college demographic will have a mirrored website for content delivery so that younger demographics without cell phones can participate. Regardless of the platform, in order to inspire the next-generation of STEM professionals, the content for this demographic will also include information on career paths, college preparation tips, and opportunities and programs in their local area and can reach a broader segment than traditional classroom activities. Based on the team’s experiences in crowdsourcing civil infrastructure challenges through cyber-platforms among the general public, including competitions and games tied to social networking, efforts seeded through Columbia’s existing Harlem Consortium program can be widely propagated with messaging refined over time to maintain student interest and actions toward STEM careers. Segment 2 – Future Professionals: At the intermediate stages of the educational pipeline, the ERC will focus on the next generation of potential end users: undergraduate and graduate engineering students, whose primary venue for training will be a new interdisciplinary program in Digital Urban Engineering, fusing engineering, technology, policy and management delivered by experiential learning modules employing cyber-platforms and contextualized using testbed pilot projects. As part of their training, these students will serve as the ambassadors interfacing the other two workforce segments (public at large and decision makers) to seed and advance technology adoption programming within these communities. In the process, they will develop valuable community-engagement skills and learn how to convey the value of new technologies and innovations to diverse audiences. Moreover, this ERC’s focus on cyberinfrastructure and smart devices allows rapid scaling beyond the lead institution. In formal educational settings, open-source educator toolkits with modules tied to established ABET requirements will expand the impacts of this ERC well beyond the institutions formally involved. A more informal development activity, will involve tool kits developed and distributed to ASCE Student Chapters for the two fold purpose of not only advancing their personal knowledge base but also creating more infrastructure advocates charged with recruiting others into citizen engineering activities,

11

with participation incentivized through online competitions among the chapters. Advocacy programming introduced in each of these programs, couched within social media, will allow students to widely propagate content among peers, widening impact among engineers and non-engineers alike to create a Citizen Engineering Movement.Segment 3 - Decision Makers: While focus on the next generation critical and the public at large is central to the wider concept of citizen sensing, more rapid acceptance of a technologically-empowered infrastructure management paradigm requires career training and continuing education among practicing engineers, who have a long-standing investment in the current paradigm and a potential aversion to new technologies. The development of an online Smart Urban Infrastructure certification program, web-based continuing education modules, and “Road Shows” incorporating realistic disaster scenarios will allow the introduction of these new concepts to practitioners framed within a value proposition that demonstrates the value added to their current business practices. Each of these programs is coupled with a suite of dissemination strategies will enable participating practitioners to become technology advocates within their personal professional networks and further propagate the ERC’s research products. Thus the themes of scalable programming and broad dissemination through pre-existing social networks threads through the entire workforce development program to broaden participation and advance the Citizen Engineering Movement.

6. Innovation Ecosystem (Strategy for selection of sectors/firms, role of industry/practitioner members, role of innovation in the ERC.) Do not list potential or committed industrial or other supporters.

A Gen-3 ERC's innovation ecosystem relies on a membership-based partnership with firms and practitioner organizations across the value chain, a highly effective construct developed over the past 25 years in ERCs. Engagement of member firms / practitioner organizations through the research program, student internships, and the employment of ERC graduates in industry accelerate transfer of ERC research to industry and other users. Member-supported sponsored research projects accelerate the transfer of technology to industry and other practitioners.

The Gen-3 ERC expands the traditional ERC construct to promote the following additional features:Opportunities for large member firms or small member or small non-member firms to develop IP generated by the ERC, if the ERC member firms exercise their first right of refusal to license this IP; andPartnerships with university and/or state and local government organizations that facilitate entrepreneurship, innovation, and economic development at the local level.The Gen-3 ERC innovation ecosystem is comprised of the ERC Industrial / Practitioner Advisory Board (IPAB), with a guiding IPAB Membership Agreement, as well as Facilitators of Entrepreneurship and Innovation plus Technology Transfer / Translational Research Partners.

The ERC for Future Cities is well positioned to foster an Innovation Ecosystem. The timing cannot be better to pursue the ERC’s mission of addressing urban infrastructure crisis with citizen-engaged sensors and Big Data. All of the participating institutions are located in or close to New York City, Chicago, and Los Angeles, the nation’s most populous cities, providing extraordinary opportunities to develop an innovation framework that leverages the most complex urban infrastructure systems and landmark structures for test-bed pilot studies, engages infrastructure owners, and attract support from industry and the general public. The ERC will take advantage of this setting to create the ecosystem for researchers and students to work with practitioners, infrastructure owners, decision makers, and industry to accelerate the ERC technology commercialization and acceptance by end users. Two important programs are being established:

12

1. Industry/Practitioner Advisory Board (IPAB ) – to provide guidance on strategic planning, research opportunities, education-related activities, and the role of innovation within the center. The IPAB will comprise of key industry and end users carefully selected as illustrated in Table 2. In conjuction, a tiered fee program will facilitate IPAB interactions and two-way knowledge exchange by: identifying students and researchers of interest to members; facilitating internship/employment opportunities; promoting industry tech talks; providing opportunities for industry guided student projects and contests/challenges; encouraging research funding and collaboration; and providing opportunities for members to guide curriculum development to better prepare students for industry.

2. Entrepreneurship Program – a set of activities designed to encourage students and researchers to pursue the creation of startup companies based on ERC technology and to assimilate into the broader university, city and state, federal and international entrepreneurial ecosystems. The programs will include: Entrepreneurship Office Hours; Access to Mentors; Events; Entrepreneurship Courses (including Lean Launchpad Training); Contests/Challenges, and Startup Service Provider and Fundraising Support.

The proposed ERC will significantly benefit from the new Institute for Data Sciences and Engineering (IDSE), where the PI is a member. The mission of IDSE is to enable engineering and applied science researchers to obtain the education, resources, and collaborations necessary to translate a data-rich environment into informational discoveries that offer tremendous potential in innovation, commercial enterprise, and workforce development. The IDSE will almost double the number of Columbia’s engineering faculty over the coming years, and add an additional 400 graduate students. The ERC will capitalize on Columbia University’s investment in the Institute, bringing together expertise within each of the Institute’s six Centers, but most especially the Smart Cities, Cyber-Security and Foundations of Data Science Centers. The Engineering Research Center for Future Cities will also take advantage of the IDDE’s strong emphasis on entrepreneurship and active engagement with Industry including young starups.

The ERC will also collaborate closely with the participating institution’s technology transfer offices to enable the successful transfer of knowledge and intellectual property to companies, as well as with a diverse and growing set of industry interaction and entrepreneurship programs at the city, state, and federal levels. As an example, the Future Cities ERC will work hand-in-hand with Columbia Technology Ventures which has extraordinary experience not only as one of the most successful university tech transfer offices (with a staff of 45, 1700 patent assets, and an average of 15 startups annually) but also in shepherding translational research and proof-of-concept initiatives.

In addition, the ERC will leverage Columbia University’s leadership role in the NYC Regional Innovation Node of the NSF’s I-Corps program – an extraordinary initiative empowering NSF funded researchers to become entrepreneurs – as well as Notre Dame’s NSF CDI project – which engages the general public to collect or analyze data, run analyses and propose conceptual designs showing that citizens extremely effective “intelligent sensors.”

Finally, the ERC will foster innovation and collaboration among people with diverse technical and cultural backgrounds to converge on shared goals and solve complex urban infrastructure problems. The proposal team has significant experience and track records in engaging female and underrepresented minorities into research. Almost a half of the Columbia Engineering undergraduates are female, the majority with research experience, and two participating underrepresented minority partner institutions will engage large numbers of minority students.

Table 2 ERC IPABIndustry Role

13

Cell phone and Internet service Smartphone-based citizen sensorsIT service Cloud computing, distributed computing, databaseSoftware, game Games to engage citizens as sensors

Tutorials and apps for end user outreachRemote sensing and GIS Optical and radar images, GIS, GPSConsumer electronics Sensors and networks for civil infrastructure monitoring Smartphone & PC manufacturers Future smartphone, tablets, and PCInfrastructure owners Test-beds, end users –asset management, resilient design strategiesGovernment agencies Test-beds, end users - emergence response, urban planningConstruction End users – resilient and sustainable developmentCode authorities Specifications, codifications, regulations

References Cited: (maximum 3 pages) See GPG for format guidelines.

Au, Y. A., Carpenter, D., Chen, X. G., and Clark, J. G. (2009). "Virtual organizational learning in open source software development projects." Information & Management, 46(1), 9-15. Catbas, F.N. and Kijewski-Correa, T., (2013) “Structural Identification of Constructed Systems: A Collective Effort Toward an Integrated Approach that Reduces Barriers to Adoption,” Journal of Structural Engineering, Special Issue on State-of-the-Art in Structural Identification, ASCE, in press.

Catbas, F.N., Kijewski-Correa, T., and Aktan, A.E.,(2013) “Structural Identification of Constructed Systems Approaches, Methods, and Technologies for Effective Practice of Structural Identification”, ASCE, Reston, VA

Banerjee, S., and Shinozuka, M. (2007), “Nonlinear Static Procedure for Seismic Vulnerability Assessment of Bridges”, Journal of Computer-Aided Civil and Infrastructure Engineering (CACAIE), Vol. 22, No. 4, pp. 293–305.

Chen Y., Feng M. Q. and Tan C-A (2006), “Modeling of Traffic Loads for System Identification of Bridge Structures.” Computer-Aided Civil and Infrastructure Engineering, (21), pp. 57–66

Chen, Y., Feng M. Q., and Soyoz, S. (2008), “Large-Scale Shaking Table Test Verification of Bridge Condition Assessment Methods” ASCE Journal of Structural Engineering, Vol. 134, No. 7., pp. 1235-1245.

Feng, M. Q. (1994). “An Optical Fiber Sensor for Measurement of Dynamic Structural Response”, Journal of Intelligent Material Systems and Structures, Vol. 5, No. 6, pp. 847-853

Feng, M. Q, Suzuki, H, and Yokoi, I (1995). “Development of Optical Sensing Systems for Smart Civil Infrastructure”, Journal of Smart Materials and Structures, No. 4, Vol. 1995, pp. A114-A120.

Feng, M. Q. (1996). “An Experimental Study on an Electro-Optical Displacement Sensor”, Nondestructive Testing and Evaluation, Vol. 13, pp. 5-14.

Feng, M. Q. (1998). “An Electro-Optical Accelerometer and Its Field Testing”, Journal of Engineering Mechanics, ASCE, Vol. 124, No. 5, pp. 513-519..

Feng, M. Q. and Kim, J. M. (1998). “Identification of a Dynamic System Using Ambient Vibration Measurements”, Journal of Applied Mechanics, ASME, Vol. 65, No. 2, pp. 1010-1023.

14

Feng, M. Q. and Bahng, E. Y. (1999). “Damage Assessment of Jacketed RC Columns Using Vibration Tests”, Journal of Structural Engineering, ASCE, Vol. 125, No. 3, pp. 265-271.

Feng, M. Q., Kim, D. K., Yi, J. H., and Chen, Y.B., (2003). “Baseline models for bridge performance monitoring”, Journal of Engineering Mechanics, ASCE. Vol. 131, No. 5, pp. 562-569.

Feng M. Q. and Kim, D.H. (2006), “Novel Fiber Optic Accelerometer System Using Geometric Moire Fringe” Sensors and Actuators, A: Physical, Vol. 128, pp. 37-42.

Feng, M.Q. (2009), “Application of Structural Health Monitoring in Civil Infrastructure”,Smart Structures and Systems, Vol. 05, No. 4, pp. 469-482. Feng, M.Q., Zou, L., Imai, M. (2010)

“Detection of Ceramic Cracks Using a Distributed High-Resolution Brillouin Fiber Optic Sensor. SICE Journal of Control, Measurement, and System Integration, Vol. 3, No. 4, pp. 279-284.

Fuchs, E. “Governing the Twenty-First Century City,” Journal of International Affairs, Spring/Summer 2012a, Vol. 65, No.2, pp. 43-57

Fuchs, E. “ Innovative Programs in Workforce Development: UMEZ Career Opportunities in Healthcare”. Upper Manhattan Empowerment Zone, September 2012b

Fuchs, E.. Innovations in City Government: The Case of New York City’s Workforce Development System. NYC Department of Small Business Services, November 5, 2008

Fuchs, E."The Permanent Urban Fiscal Crisis," chapter in Julia Vitullo-Martin, ed., Breaking Away -- Innovations in Urban America; Essays in Memory of Robert F. Wagner Jr. The Twentieth Century Fund Press, 1996

Fuchs, E.. Mayors and Money: The Politics of Fiscal Policy in New York and Chicago, Chicago: University of Chicago Press, 1992

Fukuda, Y., Feng, M.Q., and Shinozuka, M. (2010) “Cost-Effective Vision-Based System for Monitoring Dynamic Response of Civil Engineering Structures”. Journal of Structural Control and Health Monitoring, Vol. 17, No. 8, pp. 918-936

Frizzarin, M., Feng, M.Q., Franchetti, P., Soyoz, S., Modena, C. (2010), “Damage detection based on damping analysis of ambient vibration data”, Journal of Structural Control and Health Monitoring, Vol. 17, No. 4, pp. 368-385

Gomez, H., Fanning, P.J., Feng, M.Q., and Lee, S. (2011), “Long-Term Monitoring of a Curved Concrete Box Girder Bridge”, Engineering Structures, Vol. 33, pp. 2861-2869.

Goodchild, M.F. (2007). “Citizens as sensors: the world of volunteered geography”. GeoJournal 69 (4): 211-221.

Kijewski-Correa, T., Kilpatrick, J., Kareem, A., Kwon, D.K., Bashor, R., Kochly, M., Young, B.S., Abdelrazaq, A., Galsworthy, J., Isyumov, N., Morrish, D., Sinn, R.C. and Baker, W.F. (2006), “Validating the Wind-Induced Response of Tall Buildings: A Synopsis of the Chicago Full-Scale Monitoring Program,” Journal of Structural Engineering, ASCE, 132(10): 1509-1523.Boston: Kijewski, T., Brown, D., and Kareem, A. (2003), “Identification of Dynamic Properties of a Tall Building from Full-Scale Response Measurements,” Proceedings of 11th International Conference on Wind Engineering, Lubbock, TX, 2-5 June. Seoul: Pirnia, J.D., Kijewski-Correa, T., Abdelrazaq, A., Chung, J., and Kareem, A., (2007) “Full-Scale Validation of Wind-Induced Response of Tall Buildings: Investigation of Amplitude-Dependent Dynamic Properties,” Structures Congress 2007, May 16-19, Long Beach, CA. Dubai:Kijewski-Correa, T., Su, S. and Montestruque, L. (2012) “A Citizen-Centric Health Monitoring Paradigm Using Embedded Self-Locating Wireless Sensor Networks,” Proceedings of Structures Congress 2012, 20th Analysis and Computation Specialty Track, March 29-31, Chicago.

15

Kijewski-Correa, T., Kwon, D.K., Kareem, A., Bentz, A., Guo, Y. and Bobby, S. (2013) “SmartSync: An Integrated Real-Time Structural Health Monitoring and Structural Identification System for Tall Buildings,” Journal of Structural Engineering, Special Issue on State-of-the-Art in Structural Identification, ASCE., October: 271-287.Kijewski-Correa, T. (2011) “Open-Sourcing the Design of Civil Infrastructure: A Paradigm Shift,” Proceedings of Structures Congress, April 14-16, Las Vegas, NV.

Kijewski-Correa, T., Su, S. and Montestruque, L. (2012) “A Citizen-Centric Health Monitoring Paradigm Using Embedded Self-Locating Wireless Sensor Networks,” Proceedings of Structures Congress 2012, 20th Analysis and Computation Specialty Track, March 29-31, Chicago

Moore, G. A. (2005). Inside the Tornado. Collins Business Essentials, New York.

Torbal, M., Gomez, H. and Feng, M.Q. (2013), “Fragility Analysis of Highway Bridges Based on Long-Term Monitoring Data”, J. of Computer-Aided Civil and Infrastructure Engineering, Vol. 28, Issue. 3, pp. 178-192.

Schneider, B. (1983). "An Interactionist Perspective on Organizational Effectiveness." Organizational Effectiveness: A Comparison of Multiple Models, K. Cameron, and D. A. Whetten, eds., Academic Press, Orlando, 27.Shinozuka, M., Feng, M. Q. Lee, J., and T. Naganuma, (2000). “Statistical analysis of fragility curves”, Journal of Engineering Mechanics, ASCE, 126(12), 1124-1131.

Shinozuka, M., Feng, M. Q., Kim, H. K., and Kim, S. H (2000). “Nonlinear static procedure for fragility curve development”, Journal of Engineering Mechanics, ASCE, 126(12), 1287-1296.

Shinozuka, M., and Chang, S.E. (2004), “Evaluating the Disaster Resilience of Power Networks and Grids”, in Modeling Spatial Economic Impacts of Disasters, Springer-Verlag Edited by Y. Okuyama and S.E. Chang, 2004, pp 289-312.

Shinozuka, M, Liang, J and Feng, M.Q. (2005), “Use of Supervisory Control and Data Acquisition for Damage Location of Water Delivery Systems”, Journal of Engineering Mechanics, ASCE, Vol. 131, No. 3, March 1, pp. 225-230.

Shiraki, N, Shinozuka, M., Moore II, J.E., Chang, S.E., Kameda, H, and Tanaka, S, “System Risk Curves: Probabilistic Performance Scenarios for Highway Networks Subject to Earthquake Damage”, Journal of Infrastructure Systems, ASCE, Vol. 13, No. 1, March 1, 2007, pp. 43-54.

Zhang, J. and You, S. (2010), Supporting web-based visual exploration of large-scale raster geospatial data using binned min-max quadtree. Proceedings of SSDBM’10, pp. 379-396.

Zhang, J. and You, S. (2010), Dynamic Tiled Map Services: Supporting Query-Based Visualization of Large-Scale Raster Geospatial Data. Proceedings of COM.Geo’10,.

Zhou, Y, Banerjee, S, and Shinozuka, M. (2010), “Socio-economic effect of seismic retrofit of bridges for highway transportation networks: a pilot study”, Structure and Infrastructure Engineering, Vol. 6, Issue. 1–2, 2010, pp. 145–157.

16

West, J., and O'Mahony, S. (2008). "The Role of Participation Architecture in Growing Sponsored Open Source Communities." Industry and Innovation, 15(2), 145-168.

Jacob, K., Deodatis, G., Atlas, J., Whitcomb, M., Lopeman, M., Markogiannaki, O., Kennett, Z., Morla, A., Leichenko, R. and Vancura, P. (2011). “Responding to Climate Change in New York State: The ClimAID Integrated Assessment for Effective Climate Change Adaptation in New York State: Transportation,” Annals of the New York Academy of Sciences, Vol. 1244, No. 1, pp. 299-362.

Tantala, M., Nordenson, G., Deodatis, G. and Jacob, K. (2008). “Earthquake Loss Estimation for the New York City Metropolitan Area,” Soil Dynamics and Earthquake Engineering, Vol. 28, Nos. 10-11, pp. 812-835.

Franco, G., Green, R., Khazai, B., Smyth, A. and Deodatis, G. (2010). “Field Damage Survey of New Orleans Homes in the Aftermath of Hurricane Katrina,” Natural Hazards Review, ASCE, Vol. 11, No. 1, pp. 7-18.

Thiagarajan A., et. al., “VTrack: Accurate, Energy-aware Road Traffic Delay Estimation using Mobile Phones,” In SenSys, 2009.Rana R.K., et. al. “Ear-phone: an end-to-end participatory urban noise mapping system,” In IPSN, 2011.Eriksson J., Girod L., Hull B., Newton R., Madden S., Balakrishnan H., “The pothole patrol: using a mobile sensor network for road surface monitoring,” MobiSys, 2008.Karl S. Ni, Zachary Sun, Nadya T. Bliss, Noah Snavely: Construction and exploitation of a 3D model from 2D image features. Computational Imaging 2010: 75330.Gregory D. Saxton, Onook Oh, Rajiv Kishore: Rules of Crowdsourcing: Models, Issues, and Systems of Control. IS Management 30(1): 2-20 (2013)Frank Stajano, Neil Hoult, Ian J. Wassell, Peter Bennett, Campbell Middleton, Kenichi Soga: Smart bridges, smart tunnels: Transforming wireless sensor networks from research prototypes into robust engineering infrastructure. Ad Hoc Networks 8(8): 872-888 (2010)Qiang Zhu and Eamonn Keogh: Using CAPTCHAs to Index Cultural Artifacts. IDA 2010: 245-257Alon Halevy , Peter Norvig , Fernando Pereira, The Unreasonable Effectiveness of Data, IEEE Intelligent Systems, v.24 n.2, p.8-12, March 2009

A37. Chen, Y. B. and Feng, M.Q., (2003). “Technique to improve the empirical mode decomposition in the Hilbert-Huang transform”, Journal of Earthquake Engineering and Engineering Vibration, Vol. 2, No. 1, pp 75-86.

17

A51. Kim, D.H. and Feng M. Q. (2007), “Real-Time Structural Health Monitoring Using a Novel Fiber Optic Accelerometer System”, IEEE Sensors Journal, Vol. 7, No. 4, pp. 536-543.

A53. Chen, Y., Feng M. Q., Soyoz, S. (2008), “Large-Scale Shaking Table Test Verification of Bridge Condition Assessment Methods” ASCE Journal of Structural Engineering, Vol. 134, No. 7., pp. 1235-1245.o

A55. Soyoz, S., and Feng M.Q. (2008) “Instantaneous Damage Detection of Bridge Structures and Experimental Verification”, Journal of Structural Control and Health Monitoring. Vol 15 No 7 pp 958-973.

A57. Soyoz, S., and Feng M.Q. (2009) “Long-Term Monitoring and Identification of Bridge Structural Parameters”, Computer-Aided Civil and Infrastructure Engineering, Vol. 24, pp.82-92.

A62 Frizzarin, M., Feng, M.Q., Franchetti, P., Soyoz, S., Modena, C. (2010), “Damage detection based on damping analysis of ambient vibration data”, Journal of Structural Control and Health Monitoring, Vol. 17, No. 4, pp. 368-385.

A63 Chen, Y., Feng, M.Q., and Tan, C.A. (2009) “Bridge Structural Condition Assessment Based on Vibration and Traffic Monitoring” Journal of Engineering Mechanics, Vol. 135, No. 8, pp. 747-758.

A64 Yi, J., Kim, D.K., and Feng, M.Q. (2009), “Periodic seismic performance evaluation of highway bridges using structural health monitoring system”, Structural Engineering and Mechanics, Vol. 31, No. 5, pp. 527-544.

A66 Feng, M.Q. (2009), “Application of Structural Health Monitoring in Civil Infrastructure”, Smart Structures and Systems, Vol. 05, No. 4, pp. 469-482.

A67 Franchetti, P., Modena, C., and Feng, M.Q. (2009), “Nonlinear Damping Identification in Precast Prestressed Reinforced Concrete Beam”, Computer-Aided Civil and Infrastructure Engineering, Vol. 24, pp. 577-592.

A70 Chen, Y. and Feng, (2009) “Structural Health Monitoring by Recursive Bayesian Filtering” Journal of Engineering Mechanics, Vol. 135, No. 4, pp. 231-242.

A74 Fukuda, Y., Feng, M.Q., and Shinozuka, M. (2010) “Cost-Effective Vision-Based System for Monitoring Dynamic Response of Civil Engineering Structures”. Journal of Structural Control and Health Monitoring, Vol. 17, No. 8, pp. 918-936

A75 Soyoz S., Feng M.Q. and Shinozuka M. (2010) “Structural Reliability Estimation with Vibration-Based Identified Parameters”, Journal of Engineering Mechanics, ASCE, 136 (1), 100-106.

A76 Kwon, S.J. and Feng, M.Q. (2010) “An experimental study for characteristics evaluation of cement mortar using infrared thermography technique:”. Journal of Korean Society of Civil Engineers, Vol. 30, No. 1A, pp. 1-7.

A77 Feng, M.Q., Zou, L., Imai, M. (2010) “Detection of Ceramic Cracks Using a Distributed High-Resolution Brillouin Fiber Optic Sensor. SICE Journal of Control, Measurement, and System Integration, Vol. 3, No. 4, pp. 279-284.

A80 Gomez, H., Fanning, P.J., Feng, M.Q., and Lee, S. (2011), “Long-Term Monitoring of a Curved Concrete Box Girder Bridge”, Engineering Structures, Vol. 33, pp. 2861-2869.

A81 Ulusoy, H., Feng, M.Q., and Fanning, P. (2012), “System Identification of a Building from Multiple Seismic Records”, Earthquake Engineering and Structural Dynamics, Vol. 40, No. 6, pp. 661-674.

A82 Roqueta, G., Jofre, L., and Feng, M.Q., (2012), “Analysis of the Electromagnetic Signature of Reinforced Concrete Structures for Nondestructive Evaluation of Corrosion Damage”, IEEE Transactions on Instrumentation & Measurement , Vol. 40, No. 6, pp. 661-674.

A83 Yi, J.H., Kim D., Go, S. Kim, J.T., Park, J.H., Feng, M.Q., and Kang, K.S. (2012), “Application of Structural Health Monitoring System for Reliable Seismic Performance Evaluation of Infrastructures”, Advances in Structural Engineering , Vol. 15, No. 6, pp. 955-967.

A84. Baek. S., Xue, W. Feng, M. Q. Kwon, S., (2012). “Nondestructive Corrosion Detection on RC structure.” Journal of Nondestructive Evaluation, vol.31, no.2, pp.181-190

18

A85 Torbal, M., Gomez, H. and Feng, M.Q. (2013), “Fragility Analysis of Highway Bridges Based on Long-Term Monitoring Data”, J. of Computer-Aided Civil and Infrastructure Engineering, Vol. 28, Issue. 3, pp. 178-192.

19