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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, machine-learning, and generative AI 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 importance of modelling for solving engineering problems, its limitations, and how model insights 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 Environmental 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.
Each learning objective maps to one of the attributes in the Washington Accord: an international agreement that stipulates the key learning outcomes for professional degrees in a number of jurisdictions around the world, including New Zealand. You can find the full list of these attributes (WA) at the website: http://www.ieagreements.org/accords/washington/"1 Define and apply key concepts involved in the practice of physics-driven modelling, including model design, implementation, quality assurance, and application under uncertainty. (WA1, WA5)2 Perform a physics-driven modelling study for an engineering problem demonstrating best practices. (WA2)3 Apply generative AI models and workflows to an engineering problem. (WA2)4 Define and apply key concepts of data-driven modelling, including data transformation, statistical analysis, cross-validation, and regression, classification & clustering machine learning algorithms. (WA1, WA5)
ENCN304, ENCI199, ENCN281, ENCN253, ENCN242, ENCN231, ENCN221, ENCN213, ENCN205, ENCN201, EMTH210
ENCI604
Students must attend one activity from each section.
David Dempsey
Alberto Ardid
The assessment for this paper comprises a group design project in Term 1 (with individual and group assessment), computer lab tutorials to be completed in both terms 1 and 2, a midterm test and the final exam.Group design projectThe course coordinator will allocate you into groups of two or three. Groups will then be required to select from a predefined project set (drawn from different Civil subdisciplines.)The project will be assigned in Week 1, and you will develop it as new competencies are taught during Term 1. You will each be required to write a short individual report of the modelling study. The model itself, a Python code, will be assessed as a group assignment.There will be an interim submission for you to checkpoint your progress.Computer lab tutorialsAcross both terms, we will explore major topics in physics-driven modelling, artificial intelligence, and machine learning. These will be illustrated during computer labs by drawing examples from different Civil subdisciplines. The labs are designed so that most activities can be completed during the assigned tutorial period, however some additional work outside of class may be required. You have until the start of the following week’s lab session to sign-off a prior tutorial assignment.Invigilated assessmentThe midterm test is worth 30% and the final exam is worth 45%.Final gradeYour final grade on this course will be calculated as the minimum of your overall internal and external (invigilated) grade.Example 1: you obtain 30 out of a maximum 35% on internal assessment and 50 out of a maximum 75% on the test and exam. Your final grade = min([30/35, 50/75]) = 66.7% (B-)Example 2: you obtain 15 out of a maximum 35% on internal assessment and 70 out of a maximum 75% on the test and exam. Your final grade = min([15/35, 70/75]) = 42.9% (D)In the case of an emergency that affects the whole course, the Course Coordinator, in consultation with the Dean, may change the nature, weighting and timing of assessments, e.g. tests and examination may be replaced with assignments of the same weight or different weight at a different time and/or date (which, under certain circumstances, may be outside the prescribed course dates).The ‘Special consideration’ process will also be used for unforeseen circumstances that adversely affect the academic performance of students individually. The usual grounds for this are described in the UC policy ‘Special Consideration Procedures and Guidelines’, and personal circumstances due to a wider emergency event may also qualify.Special ConsiderationIn the case of an emergency that affects the whole course, the Course Coordinator, in consultation with the Dean, may change the nature, weighting and timing of assessments, e.g. tests and examination may be replaced with assignments of the same weight or different weight at a different time and/or date (which, under certain circumstances, may be outside the prescribed course dates).The ‘Special consideration’ process will also be used for unforeseen circumstances that adversely affect the academic performance of students individually. The usual grounds for this are described in the UC policy ‘Special Consideration Procedures and Guidelines’, and personal circumstances due to a wider emergency event may also qualify.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.
1. All assignments must be submitted by the due date. Late submissions will not be accepted. If a student is unable to complete and submit an assignment by the deadline due to personal circumstances beyond their control, they should discuss this with the lecturer involved as soon as possible.2. It is important to remember that copying another person’s work and submitting that work as your own is plagiarism. This practice is unethical and may result in disciplinary action being taken against you. For assignments that are done in groups, it is important that all students in the group play an equal role in completing the assessment.Generative AI use in this courseIt is not practical to regulate the use of Generative AI (e.g., ChatGPT) for internal assessments on this course. Students are allowed to use these tools in whichever manner they see fit. However, you should be aware of the risks, which are described below.Research clearly shows that the unrestricted use of ChatGPT by students during mathematical education leads to decreased performance on external assessment (which happens to comprise a major part of your grade in this course). For instance, this study showed a 17% reduction in test performance, more than three grade points.The primary mechanisms leading to adverse outcomes appear to be (1) shallow learning, where AI prevents you spending sufficient time with the material to obtain a deep understanding of it, and (2) AI dependency, where overuse of the tool leads to an inability to apply methods or think critically once it is taken awayIf you intend to use Generative AI on this course, consider prompting with some basic guardrails to prevent the above impacts on your learning:You are a coding tutor helping me with a lab assignment. Here is some code I have written and the error that I am getting. Please give me some hints about how I can fix this. Do not give me the corrected code though. **paste your code and error message**.”Even with the guardrails, a helpful Generative AI will frequently just give you the solution to a problem, cheating you of the opportunity to learn it yourself.Code of Behaviour and Academic IntegrityAll students are expected to be familiar with the University’s codes, policies, and procedures including but not limited to the Student Code of Conduct, Campus Drug and Alcohol Policy, Copyright Policy, Disability and Impairment Policy, and Equity and Diversity Policy. It is the responsibility of each student to be familiar with the definitions, policies and procedures concerning academic misconduct/dishonest behaviour. More information on UC’s policies and academic integrity can be found in the undergraduate handbook as well as at:https://www.canterbury.ac.nz/about-uc/corporate-information/policieshttps://www.canterbury.ac.nz/about-uc/what-we-do/teaching/academic-integrity
Domestic fee $1,344.00
International fee $6,488.00
* 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 Environmental Engineering .