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Michael  Kane, Ph.D. - Global Resilience Institute. Boston, MA, UNITED STATES

Michael Kane, Ph.D.

Assistant Professor, Civil & Environmental Engineering, Northeastern University | Faculty Affiliate, Global Resilience Institute

Boston, MA, UNITED STATES

Professor Kane focuses on human-in-the-loop control of civil infrastructure

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Biography

Michael Kane is an Assistant Professor of Civil and Environmental Engineering external link at Northeastern University external link where his research and teaching focus on human-in-the-loop control of civil infrastructure. Prior to joining Northeastern, he served as a Fellow at the U.S. Department of Energy’s (DoE) Advanced Research Project Agency - Energy ( ARPA-E external link ) where he identified new technology development opportunities for control of civil energy infrastructure, primarily in the areas of transportation, buildings, and distributed energy resources. In 2014, he finished a PhD at the University of Michigan external link where he pursued novel ways of embedding computational intelligence into civil systems in the Laboratory for Intelligent Systems and Technology LIST external link . Outside the office and lab, he enjoys cycling, games of ultimate external link , skiing , and backpacking .

Areas of Expertise (4)

Computational Intelligence in Civil Systems

Human-in-the-Loop Control of Civil Infrastructure

Design and Engineering

Civil Energy Infrastructure

Accomplishments (4)

John L. Tishman Fellowship

University of Michigan (2009)

Kappa Theta Epsilon (professional)

National Co-Op Honor Society

Special Acts Award (professional)

U.S. Department of Energy (2015)

Chi Epsilon (professional)

National Civil Engineering Honor Society

Education (4)

University of Michigan: Ph.D., Civil & Environmental Engineering 2014

University of Michigan: M.S., Electrical Engineering- Control Systems 2011

Drexel University: M.S., Civil Engineering - Structures 2009

Drexel University: B.S., Architectural Engineering 2009

Affiliations (2)

  • American Society of Civil Engineers (ASCE)
  • Institute of Electrical and Electronics Engineers (IEEE)

Articles (3)

Development of a Scalable Distributed Model Predictive Control System for Hydronic Networks with Bilinear and Hybrid Dynamics


Journal of Computing in Civil Engineering

Michael B Kane, Jerome P Lynch, Jeff Scruggs

2018 Hydronic heating and cooling systems are growing in complexity as the buildings and other infrastructure systems they serve demand greater efficiency and become larger. Increasing the scale and interconnectivity of these systems yields higher probabilities of component failure along with computational tractability issues. This paper presents a distributed hydronic control architecture featuring scalable computations and resiliency to component failure in both the cyber and physical domains...

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Extraction of Environmental and Operational Conditions of Wind Turbines using Tower Response Data for Structural Health Monitoring


Structural Health Monitoring

Omid Bahrami, Stavroula Tsiapoki, Michael B Kane, Jerome P Lynch, Raimund Rolfes

2017 Proper normalization of structural response data that accounts for the environmental and operational conditions (EOCs) of a structure is a key step in structural health monitoring (SHM) analyses. Normalizing data based on EOCs enables a more effective comparison of damage sensitive features extracted from structural response data derived from a system operating under a wide variation in its operations. In this paper, structural response data from an operational wind turbine is used for both damage detection as well as for EOC-based data normalization in a damage detection framework...

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Improvement of the Damage Detection Performance of a SHM Framework by using AdaBoost: Validation on an Operating Wind Turbine


11th International Workshop on Structural Health Monitoring

Stavroula Tsiapoki, Jerome P Lynch, Michael Kane, Raimund Rolfes

2017 In SHM applications various damage-sensitive features can be used for making decisions regarding damage detection. In all cases, classifiers evaluate the results and make a final decision regarding the state of the structure. Often, there are discrepancies among the decisions of different classifiers, resulting in different detection performances for each damage feature. This is expected as different classifiers may be better suited for different data settings, even in data sets corresponding to the same system...

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