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This course introduces students to robust practices for the quality-assured application of mathematical models in engineering problem solving. Using simple ODE and machine-learning models drawn from Civil Engineering subdisciplines, students will apply a structured approach to model design, testing and utilisation, including key steps of conceptualisation, verification & validation, calibration, prediction, and uncertainty analysis. By the end of this course, students will appreciate the role of physics-driven and machine learning models for solving engineering problems, their limitations, and how model information must be weighed against financial, sociocultural and legal considerations.
Mathematical and numerical modelling are increasingly present in professional engineering practice. This has been supported by the growing availability of modelling software with easy-to-use interfaces and powerful workflows. Furthermore, recent advances in data science, machine learning and AI have enormous implications for the future of the engineering profession. However, there is a risk that these modern tools could be thoughtlessly used as black boxes without a proper understanding of their correct use and limitations.This course is designed to guide you through a principled modelling process, from start to end (problem to solution). Through computer labs and a design project, this course will challenge you to hone new skills of model calibration, prediction, and uncertainty analysis, and become familiar with modern machine learning concepts. Through groupwork, discussion and communication, you will have opportunities to practice complex mental skills, including decision-making, compromise, criticism, and justification.Examples will be drawn from across Civil and Natural Resources subdisciplines, including structural, transport, water, construction management and geotechnical engineering. You will have an opportunity to choose, from a predefined list, a group project that most interests you.
- Define and apply key concepts involved in the practice of physics-driven modelling, including model design, implementation, quality assurance, and application under uncertainty. - Perform a physics-driven modelling study for an engineering problem demonstrating best practices. - Define and apply key concepts of data-driven modelling, including data transformation, statistical analysis, cross-validation, and regression, classification & clustering machine learning algorithms.
Subject to the approval of the Head of Department.
Students must attend one activity from each section.
David Dempsey
Alberto Ardid
Electronic copies of course materials will be made available through Learn.
Course communicationAll communication with the class will be through lectures, Learn and email.
Any student who has been impaired by significant exceptional and/or unforeseeable circumstances that have prevented them from completing any major assessment items, or that have impaired their performance such that the results are not representative of their true level of mastery of the course material, may apply for special consideration through the formal university process. The applicability and academic remedy/action associated with the special consideration process is listed for each assessment item below. Please refer to the University Special Consideration Regulations and Special Consideration Policies and Procedures documents for more information on the acceptable grounds for special consideration and the application process.Resit tests and exams will be held in person between 8 and 12 of July 2024. Students must be available to take the resit test or exam in person. Students with pending Special Consideration applications are advised to take the resit but will not benefit from it unless their application is eventually approved at the appropriate level of severity.All communication associated with the arrangement of equivalent alternative tests/exams will be conducted using official UC email accounts. Students will have a clearly specified amount of time to respond to the offer to sit the alternative assessment. Failure to respond will be interpreted as a declined offer. If the offer is declined or no response is received in the specified time frame, the original assessment mark will be used to compute the course grade.
Domestic fee $1,197.00
International fee $6,000.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Civil and Natural Resources Engineering .