MABY5345 Water Infrastructures and Sensor Networks Course description

This course is about the technologies required for building and retrofitting urban water pipe systems. The course covers both dig and NO-dig methods, as well as groundwork in infrastructure projects (e.g. district heating systems, electricity supply cables, and broadband). It addresses how to adapt piping systems for sustainable surface water management and emphasizes the importance of lifelong infrastructure management, taking into consideration climate change impacts and environmental risks.

Additionally, the course seeks to enhance understanding of urban water infrastructure, which includes drinking water supply, sewage pipe network, and wastewater treatment works. It provides insights into the use of sensors and models for decision-making and system diagnostics in urban water systems. The students will learn about sensor data utilization for decision support and diagnostic purposes, and model-based inference to understand a system's state.

Real-life cases will be used for teaching, and software such as Matlab, Python, SWMM, Mike+, Epanet, Scalgo, or REN grøft.

No formal requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student will

• have insight in strategies to build robust pipe systems with reduced groundwork
• have knowledge in various sensor types used in urban water
• know about current state-of-the-art of sensor technology and their usage in model-based decision support, strategies for establishing robust pipe systems requiring reduced ground work and digging
• have knowledge about trenches, developing of NO-dig methods and decision models for when to use the different methods
• have insight in holistic methods for ground works also in relation to non-water infrastructure (district heating, energy supplying and broadband cables).

Skills:

the student can

• design and evaluate technology and systems in water resource engineering
• design and implement hydraulic models
• do sensor data analysis and reconciliation
• carry out critical analysis of uncertain or incomplete information in design and optimization of urban pipe network systems
• use a model to assess sensor data and balance conflicting data.

General competence:

the student is able to

• understand the resource efficiency of urban pipe systems
• use advanced models for stormwater
• to evaluate different urban water systems
• put knowledge into practice and solving complex problems
• perform critical assessment of sensor data.

The teaching will consist of a combination of:

• Lectures & discussions
• Independent studies including video recordings and online exercises
• Coursework assignments
• Practical use of tools and software
• Field excursions.

2 project reports (700-1000 words in addition to supporting information/appendices).

1 field excursion. Hand in of a report from the field excursion (approx. 300 words)

A written exercise for missing attendance at the field excursion.

60 % physical presence is mandatory in lectures and training. A written exercise for missing attendance.

With the development of sensing technologies, transport digitalization generates and provides numerous data from different resources. This course will introduce models and applications of transport systems analysis in the context of transport studies and gain deeper insight into how these models help with the decision‐making process. Topics to be covered include data preprocessing, travel studies and analysis of data; machine learning methods; transportation systems forecast and analyses. Moreover, the course will provide a brief introduction to future sensing technologies and deep learning methods. The methods cover by this course will closely link to real world transport problem, such as travel demand modelling, accessibility, last-mile problem and other related issues.

No formal requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills, and general competence:

Knowledge:

Upon successful completion of the course, the student will achieve knowledge about:

• terminology and models for transport studies
• statistical and machine learning methods
• advanced sensing technologies
• future development in the transport data analytics

Skills:

Upon successful completion of the course, the student is capable of:

• understanding and applying the proper knowledge and method to collect, process, and analyze transport data
• applying statistical and machine learning methods with a proper interpretation of the methods used in transport modelling
• making use of approved terminology and standardization in the field of transport analytics
• optimum use of data analysis software (Python)
• using the modelling methods to support intelligent transport system management and policy development

General competence:

Upon successful completion of the course, the student:

• has deep insight into the transport data collection and data analysis methods
• is able to apply proper methods to solve practical problems in different real-world conditions
• is able to understand and explain the results of transport models
• is able to present academic results and evaluations, both to specialists and to the general public

This course will consist of lectures, one seminar (with invited lecturers, discussions and presentations), and lab sessions to provide theoretical content and preliminary hands-on experience. The students will be involved in peer feedback and the students are given a project task to work in groups during the semester.

Two individual assignments must be approved. Students who fail to meet the coursework requirements can be given up to one re-submission opportunity.

This course overlaps 5 ECTS with MABY5340 Water Infrastructure, Trenches and No-Dig and MABY5350 Sensor Networks and Model Based Decisions Support .