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CVEN20070

Academic Year 2024/2025

Computer Applications in Civil Engineering (CVEN20070)

Subject:
Civil Engineering
College:
Engineering & Architecture
School:
Civil Engineering
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Dr David AYALA CABRERA
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This course was designed for civil and structural engineering students to learn basic computer programming in Python using statistical and data analysis packages and Geographical Information Systems GIS (GIS) using ArcGIS for Civil Engineering.

Python is a popular open source computer programming language used in many fields. In this section of the module, the fundamentals of statistical programming in Python will be covered, such as: arithmetic operations, conditionals, loops, functions, working with pandas, I/O, numpy, graphing with matplotlib, and statistical inference (parameter estimation. hypothesis testing).

GIS is a computer-based information system that enables capture, modelling, manipulation, retrieval, analysis and presentation of geographically referenced data (Worboys, 1997). GIS allows for the examination of attributes and location of spatial or coordinate data, which can reveal relationship that may not be visible otherwise. In the GIS section of the module, GIS tools will be employed to answer a number of Civil Engineering questions including: What is the optimal site for my project (airport, school, shopping centre, light rail line, etc.)? What is the visual impact of my project (e.g. wind farm considerations)? How will my project fit into the existing infrastructure? How to get to a destination in the shortest amount of time?

The key objectives of this module are:
1) Provide students with an solid foundation in coding using Python, and spatial analysis and cartography in with ArcGIS;
2) To encourage the use of computer programming and GIS skills in solving Civil Engineering related problems.

About this Module

Learning Outcomes:

On completion of this module, students will be able to:
1) Understand the role of computer programming and GIS in solving problems in Civil Engineering;
2) Demonstrate the ability to conduct data analysis and visualisation in Python using a number of Python packages;
3) Apply and evaluate functions in Python in addition to visualising and interpreting results;
4) Analyse Civil Engineering problems by generating GIS data and integrating it with existing data while demonstrating comprehension of multiple decision factors

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Computer Aided Lab

24
Autonomous Student Learning

64
Total

100

Approaches to Teaching and Learning:
Lectures & lab exercises will take place online

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Practical exam in covering Python lecture material covered in Weeks 1 to 5 and computer labs 1 to 4. Week 6 Alternative linear conversion grade scale 40% No
40
 Yes
Assignment(Including Essay): Geographical Information Systems (GIS) report building upon skills gained and material covered in Weeks 7 to 11 and computer labs 5 to 9. Week 12 Alternative linear conversion grade scale 40% No
50
 No
Quizzes/Short Exercises: Completion of computer lab exercises Week 12 Alternative linear conversion grade scale 40% No
10
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Students are given feedback after each assessment along with their mark

Fangohr, H. (2015) Introduction to Python for Computational Science and Engineering
VanderPlas, J. (2016) Python Data Science Handbook
Heywood, I., Cornelius, S., Carver, S. (2012) An Introduction to Geographical Information Systems
Chang, K. (2016) Introduction to Geographic Information Systems

Name Role
Dr David AYALA CABRERA Lecturer / Co-Lecturer
Dr Meisam Gordan Lecturer / Co-Lecturer
Dr SAPTARSHI SEN Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 09:00 - 09:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 14:00 - 14:50
Spring Computer Aided Lab Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 15:00 - 17:50
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