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.

Contact

Carnegie Mellon University

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Biography

Burcu Akinci is the head of the Department of Civil & Environmental Engineering at Carnegie Mellon University. Her research interests include development of approaches to model and reason about information-rich histories of facilities, to streamline construction and facility management processes. She specifically focuses on investigating utilization and integration of building information models with data capture and tracking technologies, such as 3D imaging, and embedded sensors and radio-frequency identification systems to capture semantically-rich as-built histories of construction projects and facility operations.

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

Artifical Intelligence
Climate-resilient Environmental Systems and Technologies
Grid-interactive, High-performance, and Electrified Buildings
Computer-Aided Design (CAD)
Cyberphysical Systems (CPS)
Energy
Computer Vision
Advanced Infrastructure Systems
Civil Engineering
Smart Infrastructure
Information and Communication Technology (ICT)‎
Intelligent Engineered Systems and Society
Construction & Building Technology
Sustainable Energy and Transportation Systems
Emerging Energy Technologies

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.”

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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.

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Industry Expertise

Construction - Commercial
Education/Learning
Electrical/Electronic Manufacturing
Energy

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

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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.

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Articles

Pruning Bayesian networks for computationally tractable multi-model calibration

Frontiers in Aerospace Engineering

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.

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Digital Twin Technologies for Autonomous Environmental Control and Life Support Systems

Journal of Aerospace Information Systems

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.

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Updating subsystem-level fault-symptom relationships for Temperature and Humidity Control Systems with redundant functions

Journal of Space Safety Engineering

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).

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