The sample course plan for Michigan Tech's civil engineering master's degree with a focus on intelligent infrastructure design provides a guide to courses and requirements.
Sample Course Plan
This sample course plan is a sample, and adjustments may be required due to curriculum changes. Students should work with their advisor to develop their individual plan. A full list of graduate course descriptions is available.
The specialty area encompasses the design of robust, resilient, digitally interconnected civil infrastructure for smart community design. Students will develop holistic design approaches to streamline the incorporation (monitoring, feedback) of all things digital into structures, transportation, geotechnical, water and waste management with a focus on minimizing environmental impact and the advancement in sustainability and resiliency. This is one possible pathway for students to attain an MS in Civil Engineering, while bridging the traditional “silos” identified within the profession.
Specialty Area Description
The Intelligent Community Design specialty area is intended to offer training for applying technology to make our communities work more sustainably and efficiently. The pathway consists of courses that provide necessary knowledge of the engineering design and analysis of infrastructure systems (e.g., transportation, water resources/environmental, structural, and geotechnical), data collection techniques (from traditional surveying to more advanced sensor and sensing techniques), and computing (machine learning, optimization, numerical simulation, and big data as it relates to infrastructure/geospatial information). Graduates of this specialty area will be able to meet emerging and rapidly-growing needs for engineers to build more intelligent communities.
Coursework
The following breakdown of courses is meant to serve as a guide when crafting a degree schedule for students interested in focusing on Intelligent Community Design. Potential courses are provided below; however, alternative courses could be selected based on the student’s interests, goals and prior education. Consultation with a faculty advisor is required.
Core Courses
4 - 5 core courses should be taken. These course serve to provide a foundation for designing different civil engineering infrastructure systems with a focus on the environment and sustainability. Courses should be selected to provide adequate breadth across the areas of civil/environmental engineering, while also providing sufficient coursework focused on design vs. systems thinking.
Structures
| Course |
|---|
| CEE 4244 - Loads for Civil Structures |
| CEE 5730 - Probabilistic Analysis and Reliability |
Water Resources
| Course |
|---|
| CEE 4507 - Water Distribution and Wastewater Collection Design |
| CEE 4640/5640 - Stormwater Management and Low Impact Development |
| CEE 5630 - Advanced Hydrology |
| CEE 5666 - Water Resources Planning and Management |
Environmental
| Course |
|---|
| CEE 4502 - Wastewater Treatment Principles and Design |
| CEE 4503 - Drinking Water Treatment Principles and Design |
| CEE 4504 - Air Quality Engineering and Science |
| CEE 4506 - Sustainable Engineering |
| CEE 5501 - Environmental Process Engineering |
| CEE 5502 - Biological Treatment Processes |
| CEE 5503 Physical-Chemical Treatment Processes |
| CEE 4505/5505 - Surface Water Quality Engineering |
Transportation
| Course |
|---|
| CEE 4020 - Computer Applications: Visualizing and Communicating Design Information |
| CEE 5190 - Sustainable Pavements |
| CEE 5401 - Advanced Pavement Design |
| CEE 5402 - Traffic Flow Theory |
| CEE 5404 - Transportation Planning |
| CEE 5417 - Transportation Design |
Geotechnical
| Course |
|---|
| CEE 4820 - Foundation Engineering |
| CEE 4830 - Geosynthetics Engineering |
| CEE 5840 - Advanced Soil Mechanics |
| CEE 5811 - Fundamentals of Soil Behavior and Engineering Laboratory |
Necessary Computing Skills
3 courses should be selected to provide necessary computing skills
Machine Learning
| Course |
|---|
| CS 4811 - Artificial Intelligence |
| CS 5811 - Advanced Artificial Intelligence |
| EE 5841 - Machine Learning |
| GE 5950 - Applied Remote Sensing and Machine Learning |
| UN 5550 - Introduction to Data Science |
Database and Data Structures
| Course |
|---|
| CS 2321 - Data Structures |
| CS 3425 - Intro to Database Systems |
| CS 4321 - Introduction to Algorithms |
| CS 5321 - Advanced Algorithms |
Optimization
| Course |
|---|
| CEE 5760 - Optimization Methods in Civil and Environmental Engineering |
| MA 5630 - Numerical Optimization |
Computer Simulation
| Course |
|---|
| CEE 5740 - Modeling of Civil Engineering Systems |
Regression/Data Mining
| Course |
|---|
| EC 4200 - Econometrics |
| FW 5412 - Regression in R |
| MA 4710 - Regression Analysis |
Data Acquisition
1 - 2 courses should be taken related to data acquisition
| Course |
|---|
| FW 4540 - Remote Sensing of the Environment |
| GE 4250 - Fundamentals of Remote Sensing |
| SU 5010 - Geospatial Concepts, Technologies, and Data |
| SU 5011 - Cadaster and Land Information Systems |
| SU 5012 - Geospatial Data Mining and Crowdsourcing |
| SU 5013 - Hydrographic Mapping and Surveying |
| SU 5142 - 3D Surveying and Modeling with Laser Scanner Data |
| SU 5300 - Geospatial Monitoring of Engineering Structures and Geodynamic Processes |
| SU 5540 - Advanced Photogrammetry – Satellite Photogrammetry |
| SU 5541 - Close-Range Photogrammetry |
Coding
1 course should be taken related to coding
| Course |
|---|
| SAT 5002 - Application Programming Introduction |
| SU 5601 - R for Geoinformatics |
Note: Selected courses would have to adhere to basic requirements of the Civil MS
program. Namely, a minimum of 15 credits must be taken within the CEE Department.
In
addition, students must take one of the following courses: CEE 5710, CEE 5730, CEE
5740, or
CEE 5760. A minimum of 18 5000-level credits must be taken; a maximum of 12 3000-
or 4000-
level courses can be used towards the 30 credit requirement. All MSCE degree requirements
and rules set forth by the Department and the Graduate School must be met in order
for a
student to finish the program.