Carnegie Mellon University

Hae Young Noh

Hae Young Noh

Assistant Professor, Civil and Environmental Engineering

Address
Civil & Environmental Engineering
Carnegie Mellon University
Pittsburgh, PA 15213-3890

Bio

Hae Young Noh is an assistant professor in the Department of Civil and Environmental Engineering and a courtesy assistant professor in the Electrical and Computer Engineering.

Her research introduced the new concept of “structures as sensors” to enable physical structures (e.g., buildings and vehicle frames) to be user- and environment-aware. In particular, these structures indirectly sense humans and surrounding environments through their structural responses (i.e., vibrations) by inferring the desired information (e.g., human behaviors, environmental conditions, heating and cooling system performance), instead of directly measuring the sensing targets with additional dedicated sensors (e.g., cameras, motion sensors). This concept brought a paradigm shift in how we view these structures and how the structures interact with us.
 
Traditionally, structures that we inhabit (such as buildings or vehicles) are considered as passive and unchanging objects that we need to monitor and control, utilizing a dense set of sensors to collect information. This has often been complicated by “noise” caused by the occupants and environments. For example, building vibrations induced by indoor and outdoor environmental and operational conditions (e.g., people walking around, traffic outside, heating system running, etc.), have been often seen as noise that needs to be removed in traditional building science and structural engineering; however, they are a rich source of information about structure, users, environment, and resources. Similarly, in vehicle engineering, researchers and engineers have been investigating control and dynamics to reduce vehicle vibration for safety and comfort. However, vibrations measured inside vehicles contain information about transportation infrastructure, vehicle itself, and driver.
 
Noh's work utilizes this “noise” to empower the structures with the ability to perceive and understand the information about users and surroundings using their own responses, and actively adopt and/or interact to enhance their sustainability and the occupants’ quality of life. Since she utilizes the structure itself as a sensing medium, information collection involves a simpler set of hardware that can be easily maintained throughout the structural lifetime. However, the analysis of data to separate the desired information becomes more challenging. This challenge is addressed through high-rate dynamic sensing and multi-source inferencing. Ultimately, her work aims to allow structural systems to become general sensing platforms that are easier and more practical to deploy and maintain in a long-term.
 
At Stanford University, Noh received her PhD and MS degrees in the CEE department and her second MS degree in Electrical Engineering. Noh earned her BS in Mechanical and Aerospace Engineering at Cornell University.

Education

PhD 2011 - Stanford University
MS 2011 - Stanford University
MS 2008 - Stanford University
BS 2005 - Cornell University

Research

Research Group: AIS
  • Smart structures through statistical learning and wireless sensors
  • Structural health monitoring, risk analysis
  • Building energy management using data-driven approaches

Publications

Journals

Noh, H., Lallemant, D., & Kiremidjian, A. S. (2014). "Development of empirical and analytical fragility functions using kernel smoothing methods," Earthquake Engineering & Structural Dynamics, DOI: 10.1002/eqe.2505.

Noh, H., Rajagopal, R., and Kiremidjian, A.S. (2013). “Sequential Structural Damage Diagnosis Algorithm Using a Change Point Detection Method,” Journal of Sound and Vibration, 332(24): 6419-6433.

Noh, H., Lignos, D.G., Nair, K.K., and Kiremidjian, A.S. (2011). "Development of Fragility Functions as a Damage Classification/Prediction Method for Steel Moment Frames Using a Wavelet-Based Damage Sensitive Feature," Earthquake Engineering and Structural Dynamics, DOI: 10.1002/eqe.1151.

Noh, H., Nair, K.K., Lignos, D.G., and Kiremidjian, A.S. (2011). "On the Use of Wavelet-Based Damage Sensitive Features for Structural Damage Diagnosis Using Strong Motion Data," ASCE Journal of Structural Engineering, DOI: 10.1061/(ASCE)ST.1943-541X.0000385.

Noh, H., Nair, K.K., Kiremidjian, A.S., and Loh, C-H. (2009). "Application of a Time Series-Based Damage Detection Algorithm to the Benchmark Experiment at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan," Journal of Smart Structures and Systems, 5(1).

Conferences

Eybpoosh, M., Berges, M. E., & Noh, H. (2015). "Effects of damage location and size on sparse representation of guided-waves for damage diagnosis of pipelines under varying temperature," SPIE Smart Structures/NDE conference, San Diego, CA.

Thorsen, A., Lederman, G., Oshima, Y., Bielak, J., & Noh, H. (2015). "Mitigating the effects of variable speed on drive-by infrastructure inspection," SPIE Smart Structures/NDE conference, San Diego, CA.

Eybpoosh, M., Berges, M. E., & Noh, H. (2015). "Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes," SPIE Smart Structures/NDE conference, San Diego, CA.

Eybpoosh, M., Berges, M. E., & Noh, H. (2015). "Temperature variation effects on sparse representation of guided-waves for damage diagnosis in pipelines," SPIE Smart Structures/NDE conference, San Diego, CA.

Park, S. & Noh, H. (2014). "Structural Parameter Estimation Using Partial Measurements in Subspace System Identification," The 6th World Conference on Structural Control and Monitoring, Barcelona, Spain.

Hae Young Noh: Using Structures as Sensors

Civil and Environmental Engineering Assistant Professor Hae Young Noh discusses her work in creating self-aware structures by using structures as sensors. Potential applications range from helping the elderly by monitoring their health through sensing their footsteps to understanding traffic conditions from vibrations in buildings.