CEG8220 : Data-Centric Ground Engineering
CEG8220 : Data-Centric Ground Engineering
- Offered for Year: 2024/25
- Module Leader(s): Dr Sadegh Nadimi
- Lecturer: Dr Ross Stirling, Dr Aleksandra Svalova, Dr David Milledge
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
A suitable undergraduate degree qualification in an engineering, physics, mathematics, geosciences or geological field.
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
This module aims to introduce the basic skills and knowledge related to data science for geotechnical and geological engineers.
Students will be able to understand the key concepts of image processing, statistical modelling, and machine learning for data processing and visualisation.
Students will be able to apply their knowledge to a practical problem using computer programming.
Outline Of Syllabus
Module Summary
This module will provide an introduction to the data science approaches in ground engineering.
Students will learn about the sources of geotechnical data, quantitative image analysis, statistical modelling, and machine learning. Students will perform computer programming to implement the methodologies.
Students will be introduced to a range of ground engineering case studies, including material characterisation, ground investigation, slope stability and geohazards.
The syllabus includes:
Sources of geotechnical data
Models and data types.
Introduction to time series.
lectures and computer tutorials on basic programming
lectures and computer tutorials on quantitative image analysis for ground engineering
lectures and tutorials on basic statistical modelling, including summary statistics, data visualisation, linear/logistic regression, clustering
lecture on the basic concept of machine learning
Learning Outcomes
Intended Knowledge Outcomes
The mapping of certain AHEPv4 learning outcomes to each intended knowledge outcome is indicated in each point. On successful completion of this module, students will have:
fluency in the description of available data, acquisition techniques, and their implication for subsequent use in the context of ground engineering (M2)
knowledge of image processing and image analysis techniques for ground engineering (M3)
basic knowledge of statistical modelling and inference for data-centric ground engineering (M1)
Intended Skill Outcomes
On completion of this module, students will have had opportunities to develop:
basic skills in computer programming in a high-level language (e.g. Matlab)
ability to extract engineering-related data from images (M2)
ability to perform summary statistics and plots, linear regression, clustering, and logistic regression in a high-level programming language (M1)
fluency in visualising multi-dimensional data (M17)
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 5 | 3:00 | 15:00 | N/A |
| Guided Independent Study | Assessment preparation and completion | 1 | 10:00 | 10:00 | Coursework preparation |
| Guided Independent Study | Assessment preparation and completion | 5 | 8:00 | 40:00 | Includes background reading and reading lecture notes for a full understanding of the material |
| Structured Guided Learning | Structured research and reading activities | 10 | 2:00 | 20:00 | Structured guided learning |
| Scheduled Learning And Teaching Activities | Small group teaching | 5 | 3:00 | 15:00 | Computer Tutorials |
| Total | 100:00 |
Teaching Rationale And Relationship
The module is taught as an intensive block in order to provide an immersive learning experience with a flexible integration of lecture and tutorial sessions. This format also allows part time and full time students and CPD delegates to attend. The module includes lectures to explain the theory, and small group teaching activities to explain the programming and applications in ground engineering. It allows opportunities to practice skills and it relates directly to the assessment.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Written exercise | 2 | M | 100 | Individual coursework (7 pages) |
Assessment Rationale And Relationship
Students’ acquisition of knowledge and understanding of fundamentals are assessed through individual coursework. Each student will submit an electronic report summarising the results of two programming tasks. The data will be interpreted to produce a discussion on their application.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CEG8220's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CEG8220's past Exam Papers
General Notes
N/A
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Disclaimer
The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.