Master of Science in Civil Engineering with Smart Cities and Urban Informatics Concentration (SCUI)

Master of Science in Civil Engineering with Smart Cities and Urban Informatics Concentration (SCUI)

The Smart Cities and Urban Informatics major is an innovative major proposed under the Lyles School of Civil and Construction Engineering Master of Science in Engineering degree at Purdue University. This major is part of a professional, residential degree program designed to address the growing complexities and interconnections within urban environments based on opportunities in data science, AI and challenges in city resilience. Unlike the traditional MSE/MS concentrations, which often focus on specific engineering disciplines, this major centered in applications of AI in Civil Engineering with the possibility to take courses from other units such as Industrial Engineering, Computer Engineering and Computer Science, and insights from the Mitchell E. Daniels, Jr. School of Business. The curriculum is designed to address the multifaceted challenges of urban environments, integrating principles of urban AI, resilience engineering, and smart city technology.

This program addresses the critical need to train skilled professionals who can navigate and resolve complex problems in civil and urban infrastructure. Increases in urbanization, the impacts of climate change, global health pandemics, and rapidly evolving technological advancements that can outpace urban policy development present complex issues that city planners, resilience officers, civil engineers, data scientists, and policy makers need to address. Developed through interactions with civic leaders, industry experts, and city agencies, this program will prepare students to be future leaders in the use of engineering solutions, artificial intelligence, problem solving and technology adaptation to help address the complex issues that cities increasingly face.

Curriculum Requirements:

Each student in the program must complete a total of 30 credits. The 30 credits are divided as follows:

  • A minimum of 18 credits from the College of Engineering
  • Core Courses (9 credits)
  • Major Courses (15 credits)
  • Capstone Projects (6 credits)

Core Courses:

All students in the proposed major must take the following core courses totaling 9 credits:

  • 1 Urban Data Science course (3 credits)
  • 1 Resilience Engineering course (3 credits)
  • 1 Computational Statistics, Applied Math, and Data Visualization course (3 credits)

Major Courses:

Major Courses are to be chosen from the following designated “Urban Data Science Methods”, “Urban Data Science Technologies”, and “Computational Statistics, Applied Math, and Data Visualization” course lists. Each student should take at least 2 courses (3 credits each) in the three modules including core courses. All core courses in each module will be offered in Indianapolis. After preliminary discussions at least seven faculty have agreed to teach the courses in Indianapolis. Other courses will also be offered in Indianapolis after discussion with the faculty and incentive program.

Module 1. Urban Data Science Methods

  • CE 564: Data Science for Smart Cities (Core)
  • CE 597: Foundations of Network Models
  • CE 597: Image-based Sensing
  • CE 508: Geographic Information Systems
  • CE 507: Geospatial Data Analytics
  • CE 529: Smart Construction
  • CE 597: Asset Management of Underground Infrastructure
  • CE 597: Introduction to Applied Computer Vision in Engineering

Module 2. Urban Data Science Technologies

  • CE 569: Smart Logistics (core)
  • CE 597: Disaster Resilience and Society
  • AGRY 545: Remote Sensing of Land Resources
  • ASM 540 Geographic Information System (GIS) Applications, 3 credits
  • CE 529: Smart Construction (core)
  • CE 522: Computer Applications in Construction
  • CE 525: Built Environment Modeling
  • CE/POL 597: Sustainable and Resilient Systems: Behavior, Institutions, and Infrastructure (this can be integrated with “CE597 Disaster Resilience and Society” above)
  • CE 597: Sustainable Design
  • CE 597: Pipeline Condition Assessment and Integrity Management
  • CE 597: Development of Underground Space
  • CE 597: Intelligent Transportation Systems

Module 3. Computational Statistics, Applied Math and Data Visualization

  • STAT 51100: Statistical Methods
  • STAT 51200: Applied Regression Analysis
  • STAT 51400: Design of Experiments
  • MA 51100: Linear Algebra
  • MA 52700: Advanced Mathematics for Engineers and Physicists I
  • MA 52800: Advanced Mathematics for Engineers and Physicists II
  • CE 566: Network Models for Connected and Autonomous Vehicles
  • CE 508 - Digital Mapping for Geographic Information Systems or CE 661: Algorithms in Transportation (core)
  • MGMT 57100: Data Mining
  • MGMT 57300: Optimization Modeling with Spreadsheets
  • MGMT 59000: Machine Learning
  • MGMT 59000: Visual Analytics
  • MGMT 59000: Analyzing Unstructured Data
  • MGMT 67000: Business Analytics
  • MGMT 67200: Advanced Business Analytics
  • ECON 57600: Statistical & Machine Learning
  • IE58000: Systems Simulation

Capstone Projects:

  • Students interested in solving real world problems will work with a government agency, private sector, or Purdue professor to gain hands-on experience on solving a data science problem to solve sustainable cities issues (6 credits). This will be offered in Indianapolis.