About

Branko Glisic is a Professor and Chair of Civil and Environmental Engineering at Princeton University. His research focuses on Structural Health Monitoring (SHM), smart structures, and heritage structures, with an emphasis on advanced sensing technologies, universal SHM methods, data analysis and management, and prognostics and decision-making theory. His work includes the development and application of various sensing techniques such as fiber optic sensors, large area electronics sensing sheets, radio-frequency tags, conductive polymers, and ground penetrating radar, as well as numerical, analytical, and machine learning-based methods for structural analysis, material identification, and performance prediction. Dr. Glisic's application domains encompass concrete, steel, and masonry structures, including bridges, buildings, pipelines, coastal protections, and historical monuments. He holds a PhD in Civil Engineering and Engineering from the Swiss Federal Institute of Technology, Lausanne (EPFL), and has a background in theoretical mathematics and civil engineering from the University of Belgrade. In addition to his primary faculty role, he holds concurrent appointments at Princeton, including faculty associate positions at various institutes and centers. His contributions have been recognized through numerous awards, including the ISHMII Fellowship, the Structural Health Monitoring Person of the Year Award, and teaching excellence honors. His work integrates engineering with the arts and heritage preservation, utilizing innovative visualization and documentation techniques such as Virtual Tours, Information Modeling, and Augmented Reality.

Research topics

• Structural engineering
• Computer Science
• Engineering
• Materials science
• Machine Learning
• Data Mining
• Artificial Intelligence
• Physics
• Medicine
• Real-time computing
• Acoustics
• Geology
• Electrical engineering
• Mathematics
• Optics
• Composite material
• Forensic engineering

Selected publications

• Smart structures in civil engineering: innovations in strain sensing techniques, associated data analysis, and adaptable structures (Conference Presentation)

  2026-04-15

  article1st authorCorresponding
  PublisherDOI

• Temperature distribution along the cross-section of prestressed double-T slab during long-term monitoring (Conference Presentation)

  2026-04-15

  articleSenior author

  This research studies the long-term evolution of temperature distribution along the cross-section of a prestressed double-T slab of the Stadium Drive Garage at Princeton University. An SHM system with embedded Fiber Bragg Grating (FBG) strain and temperature sensors, supplemented by surface-mounted sensors, provides continuous measurements across the slab multi-directionally. The study focuses on capturing spatial and temporal variations in temperature fields. Preliminary observations highlight non-uniform thermal responses under environmental exposure during long-term monitoring. The findings aim to improve thermal compensation strategies, support interpretation of long-term SHM data, and inform the design of future instrumented concrete infrastructure.

  PublisherDOI

• Contributors

  Elsevier eBooks · 2026-01-01

  book-chapterOpen access
  PublisherDOI

• Advanced Structural Health Monitoring of Masonry Buildings Using Self-sensing Bricks and Cointegration Theory

  Lecture notes in civil engineering · 2026-01-01

  book-chapter
  PublisherDOI

• Restoring pedestrian bridge monitoring system with long-term disruptions (Conference Presentation)

  2026-04-15

  articleSenior author

  Bridges are critical components of city infrastructure, and their long-term performance is affected by aging and human-induced actions. Structural health monitoring (SHM) systems have been developed to capture the structural changes, but equipment failure, power shortage, or limited system accessibility can cause extended disruption. Restoring the SHM system after such a long disruption could be difficult due to missing data and unknown conditions, which in turn makes assessment of structural health condition and performance of the structure particularly challenging. This paper presents a case study on restoring the SHM system of Streicker Bridge at Princeton University. The system was initially equipped with 100+ embedded fiber Bragg grating (FBG) strain and temperature sensors since 2009. The system was interrupted starting from 2017 for nearly eight years and was restored in May 2025 with 64 accessible sensors. Structural and data analysis were performed with the focus on conditions before and after the disruptions to investigate the change in the bridge’s structural condition and performance.

  PublisherDOI

• Data-driven and model-based strategies for static monitoring of historic masonry structures

  Procedia Structural Integrity · 2026-01-01

  articleOpen access

  Masonry structures represent a substantial portion of the built environment in Italy and across Europe, often embodying considerable historical, cultural, and architectural value. Their preservation is increasingly challenging due to material degradation, inadequate maintenance, the climate change impact but most importantly as a consequence of recurrent seismic events. In this context, Structural Health Monitoring (SHM) represents a valuable strategy for achieving this objective. Additionally, given the specific peculiarities characterizing masonry structures, static monitoring emerges as a particularly suitable approach for the SHM of historical buildings. Nevertheless, environmental and operational conditions variability can introduce undesired trends in measured strain time series, potentially concealing structural response alteration associated with damage. To address this, the proposed work presents a methodological comparison between two innovative static monitoring strategies aimed at such structures. The first is a fully data-driven approach integrating neural networks within the theoretical framework of nonlinear cointegration, enabling the extraction of monitoring features that are insensitive to environmental variability and sensitive to the onset of damage. These features allow the implementation of global as well as sensor-level control charts, enabling not only the identification of the damage occurrence but also the estimation of its magnitude and location, as well as the precise identification of damage occurrence through change-point analysis techniques. The second strategy, model-based, is rooted in the theory of model class selection: by employing numerical and surrogate models associated with different damage scenarios, structural identification is performed via inverse calibration, pinpointing the most probable damage scenario occurred on the monitored building, according to a proper selection criterion, and estimating its severity and localization. The present paper focuses on outlining the theoretical formulation, assumptions, and comparative scope of these two complementary methodologies. Their implementation and application to real-world monitoring data will be addressed in future work. The comparison between these approaches provides valuable insights for the advanced monitoring and safeguarding of masonry-built heritage.

  PublisherDOI

• SHM of historic buildings

  Elsevier eBooks · 2026-01-01

  book-chapterSenior author
  PublisherDOI

• Strain-based seismic damage identification in brick masonry panels through domain adversarial neural networks: a numerical study

  2026-04-15

  article

  Historic masonry structures are particularly vulnerable to seismic events, posing critical challenges for the preservation of built heritage. Structural Health Monitoring (SHM) techniques provide a promising approach for the continuous assessment of structural integrity, enabling timely and targeted retrofit interventions. However, the scarcity of real-world labelled data representing diverse damage scenarios limits the development of robust predictive models for damage identification. To partly address this challenge, this study presents a methodology based on finite element micromechanical modelling and Domain Adversarial Neural Networks (DANN) for strainbased damage identification in masonry panels. The application focuses on knowledge transfer between different panel configurations, specifically designed to investigate the dependency of the diagnostic performance on sensor characteristics. Multiple damage types are considered at varying severity levels to capture the progressive nature of structural degradation. Monitoring data are generated numerically assuming the use of smart bricks, innovative brick-like strain sensors designed for SHM of masonry structures. A core aspect of this research is the examination of how sensor distribution and quantity influence DANN performance and damage identification. By employing a domain adaptation strategy, the DANN is trained to transfer information across different structural settings, mitigating the challenges posed by limited data and varying sensor configurations. Results highlight the effectiveness of the proposed approach in identifying structural anomalies across a wide spectrum of damage types and severity levels. Overall, the methodology demonstrates significant potential for real-world SHM applications, providing preliminary insights into optimal sensor placement and system robustness for the continuous monitoring of historic masonry structures.

  PublisherDOI

• Evaluating Contributions of Emerging Technologies to Civil Infrastructure System Resilience. I: A Decision-Making Workflow

  ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · 2025-05-05 · 1 citations

  articleOpen access

  Adopting emerging technologies (ETs) and enhancing civil infrastructure (CI) system resilience is a coupled process spanning technological, organizational, social, and economic dimensions, which concern both CI stakeholders and ET providers. To this end, no unified tool provides a decision-making aid on if and how a promising ET contributes to the resilience of an infrastructure system. This paper presents a decision-making workflow to evaluate an ET’s contribution to CI system resilience, which takes the form of a logic graph with breakdown scenarios. Using this workflow, an evaluator can identify the contribution of an ET and attribute it to one or more of five resilience properties, including resourcefulness, robustness, redundancy, rapidity, and an extended property- responsiveness. One case study of applying this workflow to a community’s water distribution system proves its effectiveness. The analytical capacity evaluation and a comprehensive set of applications of this methodology are presented in Part II of this two-part set of papers.

  PublisherOA PDFDOI

• The Metrocline: perspectives on instrumentation, long-term structural health monitoring, and deflection monitoring techniques

  Measurement Science and Technology · 2025-12-19

  articleOpen access1st authorCorresponding

  Abstract The article ‘The Metrocline’ appeared in the first volume of the Journal of Scientific Instruments, today’s Measurement Science and Technology. This article presented a deflection sensor—the Metrocline—based on the dial-gauge principle and proposed its application for measuring the deflection of bridges during testing. The article is short, written in the form of a product advertisement, and signed by the manufacturing company. In addition, it does not present any background research on the instrument, nor does it present a radically new instrument—dial gauges were developed at least a few decades prior to the Metrocline. Yet, the article thoughtfully tackles and inspires reflection on at least three topics: the dial gauge as a measurement instrument, long-term structural health monitoring (SHM) of civil structures and infrastructure, and techniques for deflection-based SHM. Hence, the aim of this paper is to first provide a concise overview of these three topics and then offer perspectives on their future research, development, and applications. This paper finds that dial gauges have greatly improved in terms of overall performance and that their application domain has spread into several other areas of engineering and manufacturing; however, being mature, they offer little space for new research and development. While long-term SHM has experienced important developments in recent decades, it still stands as a thriving area of current and future research, mostly related to improved sensor performance, long-term reliability, effectiveness of data analytics, and implementation policies. Deflection monitoring techniques have also experienced accelerated development and yielded two generations of deflection sensors: contact and noncontact, and two approaches: direct—based on sensors, and indirect—based on the numerical integration of strain or inclination. Still, the remaining challenges and new developments in supporting technologies, as well as the development of new types of structures and structural systems, keep this area active for current and future research.

  PublisherDOI

Recent grants

• NEESR Payload: Fiber Optic Method for Buried Pipelines Health Assessment after Earthquake-Induced Ground Movement

  NSF · $100k · 2010–2012

• Collaborative Research: Structural Identification & Health Monitoring using Temperature-Driven Data

  NSF · $120k · 2014–2018

• Fiber Optic Method for Bridge Health Assessment Based on Long-Gauge Sensors

  NSF · $200k · 2014–2017

• CPS: Medium: Collaborative Research: Scalable Intelligent Backscatter-Based RF Sensor Network for Self-Diagnosis of Structures

  NSF · $400k · 2021–2025

Frequent coauthors

• Daniele Inaudi

  82 shared

• Rebecca Napolitano

  Pennsylvania State University

  30 shared

• Daniele Zonta

  University of Trento

  28 shared

• Dorotea H. Sigurdardottir

  21 shared

• Hiba Abdel‐Jaber

  Princeton University

  20 shared

• Samuel Vurpillot

  Smartec (Switzerland)

  19 shared

• Daniele Posenato

  18 shared

• Naveen Verma

  Princeton University

  17 shared

Awards & honors

• 2018 ISHMII Fellow, by International Society for Structural…
• 2014 Highly Commended Award, CIOB International Innovation &…
• 2013 The Structural Health Monitoring Person of the Year Awa…
• 2011 Excellence in Teaching Award, by Undergraduate and Grad…
• 2017 Literary Achievement Award, Book of the Year 4th Place,…