Carnegie Mellon University

Advanced Infrastructure Systems (AIS)

The infrastructure systems of tomorrow will combine traditional physical assets with cyber-technologies like computers, networks and sensors. AIS researchers work to develop novel technological solutions to problems involving the planning, design, construction, and management/operation of built environment and infrastructure for smart and connected communities.


Our research projects are primarily problem-driven and can be catalogued by arranging them along three dimensions, namely:

  1. Targeted infrastructure systems, such as buildings, bridges, transportation, pipelines, and power grid.
  2. Methods used to solve the problem, such as artificial intelligence, system engineering, economics, and public policy.
  3. Societal needs that are addressed, such as citizens’ quality of life, efficient governance, social equity, and economic prosperity.

AIS Research Faculty

AIS Research Facilities

Examples of AIS research projects:

Structural Health Monitoring of Windmills

This project leverages improved probabilistic modeling methods to ensure the integrity of windmills for electricity generation, in order to promote decreased dependence on fossil fuels and sustainability.

  • Structural Systems
  • Probabilistic Modeling
  • Sustainability

Transportation Data Analytics

This project considers the various ways in which connected vehicles (CV) technology could lead to improved management of our systems and better social outcomes.  It leverages existing transportation related datasets, for example on vehicle registrations and inspections, to create emerging methods that would revolutionize management of transportation.  

Our initial applications are in using CV technology to improve emissions testing of passenger vehicles, and in developing methods to implement mileage-based fees to fund transportation systems. 

  • Connected Vehicles
  • Transportation Systems
  • Data Analytics and Algorithms
  • Safety and Emissions Inspections
  • Mileage-Based User Fees
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Autonomous Vehicle Systems Analysis

This project uses systems modeling methods to consider the safety and sustainability implications of the transition to autonomous vehicles in the passenger transportation system.

  • Transportation Systems
  • Systems Modelng
  • Sustainbility
  • Safety
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Multi-modal Transportation System Modeling and Decision Making

This interdisciplinary project uses economics theories, data mining models, and engineering methods, to learn travelers’ behavior, understand inter-relations among roadway, public transit, and parking systems, and ultimately facilitate the optimal decision making for transportation systems, such as pricing, operation, and planning.

  • Human Behavior
  • Data Analytics
  • Economics
  • Optimization
  • Network Theory
  • Efficiency
  • Safety
  • Sustainability
  • Social Welfare

Optimal Design of Shared Mobility Services in Transportation Networks

This project uses network flow theory, game theory and data analytics methods to optimally price and schedule shared mobility service (such as public transit, accessible transportation, first/last mile micro transit) to enhance safety, efficiency and reliability of passenger transportation systems.

  • Transportation Systems
  • Human Behavior
  • Data Analytics
  • Economics
  • Optimization
  • Network Theory
  • Efficiency
  • Safety
  • Sustainability
  • Social Welfare

Intelligent Transpottation System

Fault Detection and Diagnosis

  • Machine Learning

  • Software Verification

  • Information Modeling

  • Efficiency

  • Cost- reduction

Equitable Human Service Systems

This interdisciplinary project uses economics theories, optimization models, and engineering methods, to assist policy makers in planning and expanding their human service systems (i.e. energy, transportation, and food distribution).

In the current projects we focus on designing a more sustainable and equitable energy system. In the future the team plans to expand their analysis to transportation, food distribution, and disaster recovery systems.

  • Data Analytics
  • Economics
  • Human Behavior
  • Network Theory
  • Optimization
  • Sustainability
  • Social Welfare

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Infrastructure Sensing

Population growth, a changing climate and a limited budget make it very difficult to maintain and operate our aging infrastructure systems supporting modern life (i.e., buildings, bridges, pipelines, etc.). Fortunately, we are deploying sensing and computing devices in our environment at an accelerating rate and this can allow us to better monitor, operate and prioritize repair for our infrastructure systems. This project deals with the general set of challenges posed that arise when performing inferene on these sensor streams in a manner that is efficient, robust and generalizable.

  • Statistics
  • Internet of Things
  • Civil Infrastructure

Drone-Based Inspection of Bridges

This project uses BIM and drone techonolgy to improve bridge inspection processes. 

Human-Technology for Civil Infrastructure Security

The operation and maintenance of aging civil infrastructures (e.g., airports, power plants, and water facilities) in the built environments pose various systems security challenges that interweave natural, technical, and human decision and operational processes. While information and communication technologies enable engineers to collaborate on civil infrastructure operation and maintenance, the interactions between people and technology could introduce redundancies, confusion, delays, and errors.

This project examines a dynamic human-technology reliability analysis framework that captures, diagnoses, and predicts risks of human-technology interaction and collaboration processes in civil infrastructure operation and maintenance. The scientific methods used include stochastic modeling for data-driven human-in-the-loop simulations, computer vision and sensing techniques for human and technology performance analysis, and AI methods for automated systems safety checking.

  • Safety
  • Civil Infrastructure Systems
  • Computer Vision
  • Sensing
  • Machine Learning

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