Department Head and Professor, Civil and Environmental Engineering Carnegie Mellon University
Burcu Akinci focuses on emerging energy technologies and using AI to monitor energy use.
CMU News online
2025-07-11
Carnegie Mellon University experts are developing practical solutions for a fast-changing energy system.
"In my work, we are continuously monitoring complex HVAC systems and pairing AI with digital twins so we can detect faults, reduce waste and empower engineers to focus on higher-level problem-solving," says Burcu Akinci, head of the Department of Civil & Environmental Engineering at Carnegie Mellon University. "AI won’t replace human expertise — it will serve as a smart apprentice, helping us make better, faster decisions, build better systems and operate them more efficiently.
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.”
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.
1 min
Carnegie Mellon University experts are developing practical solutions for a fast-changing energy system. Their work modernizes infrastructure, accelerates innovation and harnesses AI for a more efficient and resilient future at a moment when the stakes for national competitiveness and public well-being have never been higher. Learn what CMU experts have to say about their Work That Matters.
2025
Ph.D.
Civil and Environmental Engineering
2000
M.S.
Civil and Environmental Engineering
1995
MBA
1993
B.S.
Civil Engineering
1991
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.
2025
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.
2024
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.
2024
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).
2024
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.
2023
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.
