This website stores cookies on your computer. These cookies are used to collect information about how you interact with our website and allow us to remember your browser. We use this information to improve and customize your browsing experience, for analytics and metrics about our visitors both on this website and other media, and for marketing purposes. By using this website, you accept and agree to be bound by UVic’s Terms of Use for web and social media privacy.  If you do not agree to the above, you can configure your browser’s setting to “do not track.”

Skip to main content

Nasrin Nazarisorkhavankalateh

  • BSc (Golestan University, 2016)

  • MSc (Iran University of Science & Technology, 2019)

Notice of the Final Oral Examination for the Degree of Master of Applied Science

Topic

Condition Assessment and Long-Term Structural Health Monitoring of Aging Reinforced Concrete Water Reservoirs

Department of Civil Engineering

Date & location

  • Monday, April 20, 2026

  • 12:00 P.M.

  • Virtual Defence 

Reviewers

Supervisory Committee

  • Dr. Rishi Gupta, Department of Civil Engineering, University of Victoria (Supervisor)

  • Dr. Sardar Malek, Department of Civil Engineering, UVic (Member)

  • Dr. Curran Crawford, Department of Mechanical Engineering, Uvic (Non-Unit Member) 

External Examiner

  • Dr. Laura Minet, Department of Civil Engineering, University of Victoria                                                                                             

                                                                                               

Chair of Oral Examination

  • Dr. Lincoln Shlensky, Department of English, UVic

     

Abstract

Structural Health Monitoring (SHM) is increasingly required to ensure the safety, serviceability, and longevity of aging reinforced concrete (RC) infrastructure. According to the Canadian Infrastructure report card 2019, many municipal water reservoirs in Canada were constructed several decades ago and are approaching or exceeding their original design service life, while continuing to operate under changing environmental and loading conditions. Traditional condition assessment approaches, primarily based on visual inspection and occasional destructive testing, are limited in their ability to provide continuous, objective, and system-level insight into structural performance.

This dissertation presents an integrated framework for condition assessment and long-term monitoring of reservoirs, with a specific focus on an in-service reinforced concrete potable water reservoir located at Mount Tolmie in Victoria, British Columbia. The proposed framework combines finite element modeling (FEM), non-destructive evaluation (NDE) methods, wireless sensor-based SHM, and Building Information Modeling (BIM) to develop an information-rich digital representation of the structure. Seismic response analysis with fluid–structure interaction is first conducted to identify structurally critical regions and guide sensor placement. A comprehensive, coring-free multi-NDE assessment which includes infrared thermography, rebound hammer, ultrasonic pulse velocity, ground penetrating radar, and non-invasive corrosion rate and electrical resistivity measurements, is then performed over multiple field campaigns to evaluate spatial patterns, repeatability, and temporal changes in material condition. Long-term sensor data, including linear displacement, tilt gauges, and acceleration measurements, are subsequently analyzed to characterize the structure’s behavior under operational conditions and to establish data-driven thresholds for anomaly detection. The novelty of this work lies in the first application of non-invasive corrosion rate and electrical resistivity measurements using the iCOR device for the condition assessment of an operating reinforced concrete potable water reservoir, together with the use of multi-year, coring free NDE results to inform structural condition assessment and the development of a BIM-based digital twin framework in which long-term, low-frequency SHM sensor data are continuously linked to the 3D model to enable near real-time monitoring and automated anomaly alerts for stakeholders. 

The outcomes of this research demonstrate the feasibility of integrating heterogeneous inspection, testing, and monitoring data into a unified digital twin model for reinforced concrete water reservoirs, supporting identification of critical zones and informed decision-making for maintenance and future monitoring of aging water infrastructure.