Carnegie Mellon University
Advanced Infrastructure Systems Research Overview

Advanced Infrastructure Systems

The infrastructure systems of tomorrow will combine traditional physical infrastructure with a cyber-infrastructure of computers, networks and sensors. The AIS research group works to develop novel technological solutions to problems involving the planning, design, construction, and management/operation of built facilities and infrastructure. Projects aim to improve performance and reduce the life-cycle cost of a broad range of physical infrastructure systems through:

  • sensing, information modeling, advanced analytics and visualization for the construction, operation and maintenance phases of infrastructure systems
  • new models, methods and tools for planning, design, project management and facility/infrastructure management; and
  • developing more sustainable processes and components that can be used in the built infrastructure

Research places emphasis on construction management, facilities operation and management, the energy efficiency of buildings, structural health monitoring, and risk assessment and decision-making.

Construction Management

The AIS research group is interested in improving the decision-making processes involved in each step of an infrastructure project. The group is developing semantically rich information models, utilizing information and communication technologies such as sensing systems, and using immersive environments to visualize and analyze data trends.

Professors Burcu Akinci and Mario Berges are using energy informatics to improve efficiency in construction, and seek to identify and improve information exchanges to streamline quality control processes. Professor Akinci and her students are also attempting to improve the quality of construction team decisions and decrease the cost of managing data by providing teams with easy access to relevant and critical information through customizable dashboards.

eeb hub

Improving Information Exchanges: How CEE researchers are using energy informatics to make construction more efficient

Imagine being able to walk through a renovated building and not only admire the newly-redesigned workspace, but also evaluate the cost and energy efficiency of every building material used inside – all before the renovations even begin.


Facilities Operation Management

The complexity of modern infrastructure and the limited resources used to maintain it have increased the need for engineers to address operation and management, and to extend their reach to the entire lifecycle of built infrastructure. AIS researchers are investigating use of advanced technology to address these needs.

In collaboration with faculty from the Robotics Institute, Professor Burcu Akinci and her students are investigating the use of 3D imaging sensors and other in-situ sensors to monitor and capture as-is conditions, and are formalizing approaches to process and fuse data collected by such sensors to generate and represent as-is Building Information Models (BIMs). Professor Akinci is also working in collaboration with the Robotics Institute to combine small aerial robots with 3D imaging techniques and planning, modeling and analysis to evaluate the health of bridges, buildings and other infrastructure. 

Energy Efficient Buildings

The AIS group is working to develop novel technological solutions to problems involving the energy inefficiency of residential and commercial buildings. Reseach is being conducted to address the current lack of detail in understanding of the electricity consumption of facilities, the fragmentation of existing building control systems, and the challenges associated with integrating buildings as active participants in the electric grid. 

Professor Mario Berges and his students are seeking to enable energy-aware smart facilities by identifying ways to provide appliance-specific information about energy consumption in buildings, and to facilitate conservation. Professor Hae Young Noh applies various algorithms including adaptive learning and Gaussian processes to predict solar energy generation and building energy consumption. With the help of her students, she seeks to create a robust monitoring system for smart structures that can be used to conserve energy and resources.

Scaife Hall

Developing Tools to Make Buildings Energy Efficient

A software platform that enables comprehensive real-time command and control of indoor environments has the potential to revolutionize the way buildings are currently managed. [MORE]

Structural Health Monitoring

Structural health monitoring allows for the automated monitoring of structural conditions in an efficient and reliable way, which helps to prevent catastrophic failures and reduce maintenance costs. The AIS group is focused on developing and implementing efficient and robust damage diagnosis algorithms that can perform well under various environmental and operational conditions.

Professor Irving Oppenheim’s research concentrates on the development of ultrasonic and MEMS (micro-electromechanical systems) devices as sensors for structural health monitoring. Professor Hae Young Noh focuses on data analytics, using statistical learning algorithms for assessing, forecasting, and controlling dynamic systems to maintain and improve their functionality and sustainability.

Professor Matteo Pozzi focuses on the practical applications of sensors on civil infrastructures and uses statistical probability to make predictions about the information the sensors collect. Professor Mario Berges and his students work with sensor and decision support systems with an aim to develop intelligent infrastructure able to communicate with the environment, as well as other infrastructure systems, making it more adaptive, autonomous and efficient. 

Risk Analysis and Decision Making

The AIS group is performing research to assess the risk related to aging, deterioration, and exposure to exceptional events of infrastructure systems. It is investigating optimal strategies for risk mitigation through several research projects, including developing probabilistic models for degrading systems and identifying optimal policies in decision-making problems for operation and management of infrastructure systems. 

Professor Matteo Pozzi seeks use probabilistic modeling to help quantify and solve infrastructure problems. His research creates a framework to handle uncertainty in situations that make prediction necessary, such as natural disasters. Professor Susan Finger’s research focuses on another part of the decision making process by studying collaborative learning in engineering design, as well as computer-supported collaborative learning.

More information about the exciting projects being done by our world-class faculty and students can be found by visiting the websites of our faculty and their individual research groups, as well as the Research Profiles section of our website.