Burcu Akinci
Department Head and Professor, Civil and Environmental Engineering Carnegie Mellon University
- Pittsburgh PA
Burcu Akinci focuses on emerging energy technologies and using AI to monitor energy use.
Biography
She earned her B.S. in civil engineering (1991) from Middle East Technical University and her M.B.A. (1993) from Bilkent University at Ankara, Turkey. After that, she earned her M.S. (1995) and her Ph.D. (2000) in civil and environmental engineering with a specialization in construction engineering and management from Stanford University.
Akinci has one patent, two patent applications, more than 60 referred journal publications, and 80 refereed conference publications. She co-edited a book on CAD/GIS integration and another book on embedded commissioning. She has graduated more than 16 Ph.D. students and 15 M.S. thesis students, and is currently advising/co-advising four Ph.D. students.
Areas of Expertise
Media Appearances
Western Pa. utilities use drones and other robots to make inspections safer, more efficient
90.5 WESA radio
2024-06-19
Drones are an ideal technology for utility inspections, according to Burcu Akinci, professor of civil and environmental engineering at Carnegie Mellon University. With poles and wires distributed over miles, human inspection takes time. With drones, “you get this bird’s eye view with data and imagery. And data that is very difficult to get in any other way.”
How Carnegie Mellon University champions women in engineering
Study International online
2020-12-01
Burcu Akinci attributes her interest in engineering to several factors, but being raised by a woman engineer is undoubtedly high on the list. Today, Akinci is the Paul Christiano Professor of Civil and Environmental Engineering at Carnegie Mellon University (CMU), where she guides the next generation of women in engineering.
Media
Industry Expertise
Accomplishments
ASCE Distinguished Member
2025
Education
Stanford University
Ph.D.
Civil and Environmental Engineering
2000
Stanford University
M.S.
Civil and Environmental Engineering
1995
Bilkent University
MBA
1993
Middle East Technical University
B.S.
Civil Engineering
1991
Affiliations
- Manufacturing Futures Institute
- Pennsylvania Smart Infrastructure Incubator
Patents
Methods and systems for linking building information models with building maintenance information
US20140089209A1
A computer-implemented method includes, in one aspect, receiving a request for a spatial analysis of building behavior of an entity within a building facility; retrieving building maintenance information about the entity within the building facility; accessing a building information model for the building facility; identifying a portion of the building information model that pertains to the entity; and based on the retrieved building maintenance information and the identified portion of the building information model, generating the spatial analysis of the building behavior for the entity within the building facility.
Articles
Pruning Bayesian networks for computationally tractable multi-model calibration
Frontiers in Aerospace Engineering2025
Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods are typically deployed on individual models and are limited in their ability to capture dependencies across models. In addition, model heterogeneity has been a significant issue in integration efforts. Bayesian Networks are well suited for multi-model calibration tasks as they can be used to formulate a mathematical abstraction of model components and encode their relationship in a probabilistic and interpretable manner. The computational cost of this method however increases exponentially with the graph complexity.
Digital Twin Technologies for Autonomous Environmental Control and Life Support Systems
Journal of Aerospace Information Systems2024
Environmental control and life support systems will require enhanced self-awareness and self-sufficiency as human spaceflights are designed to reach further destinations. These requirements have led to the development of autonomous technologies and systems to enable more Earth independence, while at the same time relying more heavily on the knowledge contained in their computational models (as opposed to the knowledge of ground control experts). For environmental control and life support systems, these consist of disparate models often tailored to specific subsystems and use cases, such as temperature control and removal.
Updating subsystem-level fault-symptom relationships for Temperature and Humidity Control Systems with redundant functions
Journal of Space Safety Engineering2024
As we aim for deep space exploration, supporting vital systems, such as the Temperature and Humidity Control System (THCS) in the Environmental Control and Life Support System (ECLSS), through timely onboard fault detection and diagnosis becomes paramount for mission success. Many existing fault diagnosis approaches assume that the function that models the relationship between faults and associated symptoms (fault-symptom relationships) will remain constant throughout the THCS’ lifetime. Therefore, many of these diagnosis methods are not robust enough to automatically account for changes in fault-symptom relationships as a result of changes in the habitat (e.g., system reconfiguration).
FSBrick: an information model for representing fault-symptom relationships in heating, ventilation, and air conditioning systems
Data-Centric Engineering2024
Current fault diagnosis (FD) methods for heating, ventilation, and air conditioning (HVAC) systems do not accommodate for system reconfigurations throughout the systems’ lifetime. However, system reconfiguration can change the causal relationship between faults and symptoms, which leads to a drop in FD accuracy. In this paper, we present Fault-Symptom Brick (FSBrick), an extension to the Brick metadata schema intended to represent information necessary to propagate system configuration changes onto FD algorithms, and ultimately revise FSRs. We motivate the need to represent FSRs by illustrating their changes when the system reconfigures. Then, we survey FD methods’ representation needs and compare them against existing information modeling efforts within and outside of the HVAC sector.
Lessons learned on the implementation of probabilistic graphical model-based digital twins: A space habitat study
Journal of Space Safety Engineering2023
Habitats for future human spaceflights will require more resilient environmental control and life support systems (ECLSS). To that end, it is important to facilitate decision making in case of unexpected failure by quantifying the uncertain and dynamic nature of the physical phenomena involved. Combining probabilistic and deterministic models is a particularly promising approach to address this issue. In particular, Probabilistic Graphical Model (PGM) based digital twins are relevant as they embed random variables evolving overtime. Previous research used this modeling method for several applications such as monitoring structural health or manufacturing processes. We envision that the space exploration sector can also benefit from this approach by using the insight gained on specific sub-systems.
