Introduction

WELCOME STUDENTS TO THE COURSE

ENGINEERING SYSTEMS AND DECISION ANALYSIS

1- Course Name: Engineering System & Decision Analysis

2- Course Code: CIVE 3066

3- Responsible Faculty: Computer Science and Engineering (Department of System Information)

4- Credit: 3 ECTS

5- Lecturers:

Dr. Nguyen-Tuan-Thanh LE (email: thanhlnt [at] tlu [dot] edu [dot] vn)

Msc. Lai Tuan Anh (email: laituananh [at] tlu [dot] edu [dot] vn)

6- Course Description:

This course provides the students with the knowledge and capacity of civil engineering systems, multi-criterion decision analysis, simulation and optimization techniques, numerical methods, statistical distributions, confidence limits, hypothesis testing.

7- Course Objectives:

At the end of this course, the successful student should be able to:

  • Demonstrate their improved understanding of the concepts of mathematical modeling and statistical data analysis as applied to civil engineering systems.

  • Estimate parameters for various statistical distributions, determine which distribution best describes a set of data and to generate random samples from those distributions

  • Demonstrate the proper application of confidence limits and hypothesis testing to examples from civil engineering systems

  • Demonstrate the proper application of simple linear or multiple regression for building empirical models of engineering and scientific data.

  • Demonstrate the use of Geographic Information Systems (GIS) for spatial data collection, organization, and analysis

  • Enhance their oral and written communication and presentation skills

8- General Class Policies:

Students are expected to:

  • Attend regularly: It is recommended that you attend each class because important information will be covered in class that will help you with the laboratory assignments, homework, and exams. Remember that not everything is in the lecture handouts. Also, if changes in exam procedure, exam date, exam coverage, assignments, etc. are announced in class you are responsible for knowing this information.

  • Access the course piazza site regularly: The course site will be updated regularly with PowerPoint handouts and other materials presented in class. The class schedule and due dates for assignments will be regularly posted and updated. It is your responsibility to be aware of this information. Anything that is posted on the course site and covered in class is likely to be subject to questions on the midterm and final.

  • Respect the lecture time: Coming late to class or leaving early from class causes a disturbance to others. Please try not to enter or leave the room while the class is in progress, except in the case it is absolutely necessary. If you must leave the classroom please do so as quietly as possible.

  • Turn off or silence your cell phones before the start of class.

  • Respect assignment deadlines: Assignments will typically be submitted via piazza.com as a private question to lecturers and submissions after the deadline will not be accepted. Unless you have discussed an emergency situation with your instructor late assignments will not be accepted. It is highly encouraged that you submit your assignment prior to the deadline to avoid any last-minute problems.

  • Be honest: All students are responsible for knowing and adhering to the academic integrity policies of this institution. Violations of this policy may include cheating, plagiarism, aiding academic dishonesty, fabrication, lying, bribery, and threatening behavior.

Platforms: MatLab or Octave

9- Homework:

Assigned weekly on the piazza, some submitted on paper, some submitted as MATLAB files or ArcGIS documents on the course site.

Show your work and clearly identify your answers.

Must be your own work (every cell, every line of code, and every word of text must be written individually). Providing your homework solution to someone is not allowed, but discussion with others is allowed. Copied work will have the note 0.

Due on Wednesday before class, late homework is not accepted.

Solutions are posted on piazza after the due date.

In the case where assignments are sent to the instructors' email, it is the responsibility of the student to be sure that all attachments to the submission are provided. Students will not receive credit for any submission that is missing the required attachments.

10- Prerequisite Knowledge and Skills:

The students are expected to have an understanding of basics statistical concepts and measures and basic concepts of simulation and optimization models.

Prerequisite course: Numerical Modeling and Risk Analysis

11- Exams and Grading

The course will include two multiple-choice quarterly, a midterm and a comprehensive final examination. Grading will be based on the following components: