Nur Sila Gulgec

I am currently a PhD candidate at Lehigh University Department of Civil and Environmental Engineering. I am working with my advisors Prof. Shamim Pakzad (CEE) and Prof. Martin Takac (ISE). Prior to that I got my MS degree from Carnegie Mellon University in 2013.

My research focuses on the intersection of machine learning and structural engineering. I am interested in structural health monitoring, anomaly detection, deep learning, machine learning, big data, sensor networks, compressed sensing, digital image correlation, and mobile sensing areas.

I am planning to complete my PhD by August 2019. I am currently looking for both academic and industrial positions.

Work and Research Experience

CEE, Lehigh University, Research Assistant, Aug 2014 to present

• Multi-Task Deep Neural Networks for Structural Damage Diagnosis
  • Developed convolutional neural network based framework for accurate, robust and evolving diagnosis of structural defects
  • Evaluated the sensitivity of the algorithm to measurement noise by using digital image correlation and presented results in a statistical framework
  • Played an active role in preparation of grant proposals to National Science Foundation (NSF), Pennsylvania Infrastructure Technology Alliance (PITA) and The Center for Integrated Asset Management for Multi-Modal Transportation Infrastructure Systems (CIAMTIS)

• Innovative Sensing for SHM using Deep Learning Framework
  • Designed a long short-term memory (LSTM) based framework to use easily and inexpensively generated structural response data into more expensive measurement estimates in SHM applications
  • Developed a novel training method to learn long sequences

• Compressed Sensing Approach for Big Data in SHM
  • Research on current challenges with big data analytics in SHM and an image-based compressive sensing approach for damage diagnosis avoiding the original signal recovery

Iron Bridge Companies, LLC. Project Engineer, 2013-2014

• Controlled cost and production of structural bridge rehabilitation projects
• Estimated quantities and cost of materials, equipment, or labor to determine project feasibility
• Assisted in coordinating, reviewing and interpreting bid and contract documents

CEE, Carnegie Mellon University, MS Student Researcher, 2012-2013

• Constructed object-oriented information modeling of dams by using Unified Model Language (UML) for the project “Instrumentation infrastructure for monitoring health and behavior of dams and levees (United States Army Corps of Engineers)” ·
• Identified information and visualization requirements for dam risk assessment

Education

Lehigh University, Bethlehem, PA, August 2019 (expected)

• Ph.D. in Structural Engineering, GPA: 3.82
• Dissertation: Big Data Approach with Deep Learning for Next Generation Structural Health Monitoring

Carnegie Mellon University, Pittsburgh, PA, May 2013

• M.S. in Civil and Environmental Engineering, GPA: 3.90

Middle East Technical University, Ankara, Turkey, June 2012

• B.S. in Civil Engineering, GPA: 3.44

Teaching Experience

Teaching Assistant:

• CEE 059 - Strength of Materials, Prof. Paolo Bocchini, Spring 2019
• CEE 159 - Structural Analysis I, Prof. Peter Mueller, Falls 2016-17
• CEE 117 - Numerical Methods in Civil Engineering, Prof. Peter Mueller, Springs 2016-17
• CEE 259 - Structural Analysis II, Corey Thomas Fallon, Spring 2017
• CEE 202 - CEE Planning and Engineering Economics, Prof. Mesut Pervizpour, Spring 2016
• CEE 011 - Surveying, Prof. Mesut Pervizpour, Fall 2016
• CEE 012 - Engineering Statistics, Prof. Tara Troy, Fall 2016

Highlighted Duties: Prepared and taught laboratory and recitation sessions; graded homework, quizzes and exams; instructed and mentored students during office hours

Research Mentor:

• Mentorship in NSF Research Experiences for Undergraduates Summers 2015, 2018, Fall 2018

Highlighted Duties: Supervised for simulating structural components, collecting data with several sensing systems and provided guidance for ABAQUS, Matlab and OpenSees

Publications

Manuscripts in Press:

• Gulgec, N. S., Takac M., Pakzad S.N.(2018). “Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization”. Journal of Computing in Civil Engineering. In production.
• Gulgec, N. S., Ergan, S., Akinci, B., Kelly, C. J. (2015). “Integrated Information Repository for Risk Assessment of Embankment Dams: Requirements Identification for Evaluating the Risk of Internal Erosion”. Journal of Computing in Civil Engineering, 30(3), 04015038
• Kasireddy, V., Ergan, S., Akinci, B., and Gulgec, N. S. (2015). “Visualization requirements of engineers for risk assessment of embankment dams.” Visualization Eng., 3(1).

Refereed Conference Proceedings:

• Gulgec, N. S., Takac M., Pakzad S.N. (2019). “Experimental Study on Digital Image Correlation for Deep Learning-Based Damage Diagnostic”. In Dynamics of Civil Structures. In production.
• Gulgec, N. S.,Takac M., Pakzad S.N. (2018). “Innovative Sensing by Using Deep Learning Framework”. In Dynamics of Civil Structures, Volume 2 (pp. 293-300). Springer, Cham.
• Gulgec, N. S.,Takac M., Pakzad S.N. (2017). “Structural Damage Detection Using Convolutional Neural Networks”. In Model Validation and Uncertainty Quantification , Volume 3 (pp. 331-337). Springer, Cham.
• Gulgec, N. S.,Takac M., Pakzad S.N. (2017). “Structural Damage Diagnosis with Time-Varying Loads Using Convolutional Neural Network”. In proceeding of Fourth Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures.
• Gulgec, N. S., Shahidi, G. S., Matarazzo, T. J., Pakzad, S. N. (2017). “Current Challenges with BIGDATA Analytics in Structural Health Monitoring”. In Structural Health Monitoring and Damage Detection, Volume 7 (pp. 79-84). Springer, Cham.
• Shahidi, S. G.,Gulgec, N. S., Pakzad, S. N. (2016). “Compressive Sensing Strategies for Multiple Damage Detection and Localization”. In Dynamics of Civil Structures, Volume 2 (pp. 17-22). Springer International Publishing.
• Gulgec, N. S., Shahidi, S. G., Pakzad, S. N. (2016). “A Comparative Study of Compressive Sensing Approaches for a Structural Damage Diagnosis”. In Geotechnical and Structural Engineering Congress 2016 (pp. 1910-1919).

Honors and Awards

• P.C. Rossin Doctoral Fellow, Lehigh University, 2017
• Doctoral Travel Grants for Global Opportunities (DTG-GO), Lehigh University, 2017
• GSS Travel Grant, Lehigh University, 2016
• Dean’s Doctoral Assistantship, Lehigh University, 2014
• Harold A. Thomas Tuition Scholarship, Carnegie Mellon University, 2012
• Dean’s High Honor List, Middle East Technical University, 2009-2011

Professional Service and Memberships

• Member, American Society of Civil Engineers (ASCE), 2018-present
• Member, Society of Experimental Mechanics (SEM), 2017-present
• Member, Fritz Engineering Research Society (FERS), 2014-present
• Voluntary, Woman in Science and Engineering, at Lehigh University, 2018
• Vice President, Secretary, Turkish Students Club, at Lehigh University, 2015-2017
• Reviewer,(ASCE) Journal of Computing in Civil Engineering, 2018
• Reviewer, Structure and Infrastructure Engineering, 2018
• Reviewer, (ASCE) Journal of Structural Engineering, 2017
• Certificate, Engineer in Training, New Jersey, 2013

Skills

Computer Skills:

• Programming languages: Python, Matlab, C
• Deep learning and data science toolkits: PyTorch, TensorFlow, Theano, MatConvNet, Jupyter
• Big data platforms: Spark, MySQL
• Simulation tools: ABAQUS, SAP2000, ANSYS, OpenSees
• Sensing tools: ARAMIS Software, LabVIEW, PC9000
• CAD tools: AutoCAD, WaterCAD, 3Ds MAX, EON
• BIM related tools: REVIT Architecture, Solibri, Navisworks, Synchro, Vico

Related Coursework:

• Lehigh University: Sensors, Signals and Systems; Random Vibrations; Pattern Recognition; Mathematical Methods in Engineering; Advanced Earthquake Resistant Structures; Information Theory; Mining Massive Datasets; Nonlinear Optimization; State-Space Control
• Carnegie Mellon University: Structural Health Monitoring; Urban Systems Modeling; Data Acquisition; Data Management; Data Mining in Infrastructure; Advanced CAD, BIM and 3D Visualization.