Civil Engineering

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. The course focuses on advanced methods and models to predict the vulnerability of a water body to natural and anthropic pressures and evaluate the risk of water scarcity or poor quality under current and future conditions. In particular, the course addresses the following main contents: analytical and numerical models of flow and contaminant transport, data-driven and risk assessment methods, and laws of similarity for model tests in hydraulics. It is divided into two modules: 

Module 1: 

• Analytical and numerical modeling of flow processes in natural domains
• Analytical and numerical modeling of transport processes
• Risk and sensitivity analysis
• Monitoring and data-driven methods for the analysis of water bodies
• Introduction to geostatistics

Module 2: 

• Dimensionless numbers and laws of similarity for model tests in hydraulics
• Hydraulic measurements

This course addresses issues surrounding the Climate Emergency and Net Zero in the renewable and sustainable energy field, with an introduction to existing energy demand and provision in the UK and globally. This involves various energy technologies, resources and devices introduced to meet the potential energy gaps and mix for future demand and supplies.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. This course advances students' understanding of structural behavior and enhances their ability to apply structural analysis methods to civil engineering structures. Students acquire knowledge in the following areas: Structural Matrix Analysis, i.e., techniques for analyzing 2D truss and frame structures using the Direct Stiffness Method and FEM software. Buckling Analysis, i.e., methods for buckling and post-buckling analysis of discrete and continuous systems, with FEM applications. Plastic Analysis, i.e., concepts of plasticity, incremental and limit analysis for truss and beam systems, also using FEM tools. The main skills developed during this course include: Proficiency in matrix analysis and the Finite Element Method (FEM) for analyzing truss and frame structures. Ability to evaluate buckling and post-buckling behavior of rigid and continuous systems, using equilibrium and energy methods. Competence in conducting plastic analysis of structural systems, including an understanding of plastic hinges. Software Proficiency: hands-on experience with FEM-based software for solving structural, buckling, and elasto-plastic problems. Analytical and critical thinking: enhanced ability to approach complex structural issues with theoretical and computational tools. The course contributes to the objectives of the master’s program related to the application of mathematical tools for interpreting, describing, and modeling structural problems.

A prior knowledge and understanding of the static behavior of planar truss and beam structures is recommended. The course includes theoretical lectures (module 1), exercises and laboratory sessions (module 2). 

Drinking water production plants and wastewater treatment plants are essential parts of the urban water infrastructure and have a large influence on the hydrological cycle. To protect the environment and the environmental services the ecosystems provide, water needs to be handled in an environmentally sustainable way. In the glocal perspective SDG 6 "Water and sanitation" targets the need to ensure availability and sustainable management of water and saniation for all. The aim of the course is to provide knowledge about water and wastewater treatment to be able to design and operate municipal facilities for production of drinking water and treatment of wastewater in the urban area.

This course introduces machine learning for Artificial Intelligence system design and implementation. Upon successful completion of this course, students should be able to explain the concepts of different machine learning models used in civil and environmental engineering areas; identify proper machine learning models and learning techniques for regression and classification tasks; apply machine learning models to generate useful information from raw data; evaluate outcomes of machine learning models based on evidence-based judgments; and create a machine learning model to generate useful information from raw data. 

Prerequisite: ENG1108 - ENGINEERING INFORMATION PROCESSING

This course develops an understanding of the causes and motivations of personal travel, the means by which movement takes place, as well as the impact personal travel, freight and transport infrastructure have on the environment, economy, and society. This is done by providing a grounding in techniques for modelling, analyzing and assessing (multi-modal) transport systems and their impacts. Transport policy and appraisal and fundamentals of data collection, as well as professional communication (presentation skills) are included. Course entry requirements: None. Co-requisites: None. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. The objectives of the unit are to gain a clear understanding of: why road safety is important, how we can achieve improvements and who is doing the work; the multidisciplinary nature of road safety and why we need to use a combination of engineering, education, and enforcement to be successful; the behavior of road users and ways in which the road environment can be designed/improved to cater for their needs; the complexity of the human/vehicle/road system and how the interrelationships work to influence the level of safety; what are the legal responsibilities of road authorities and decision makers and how they can fulfil them; how to undertake accident investigations; how to collect accident data and what to look for in quality data; how to analyze accident data, turn it into information and develop cost effective, practical counter measures; what needs to be done after treating a site and how to do it; how to be proactive in preventing accidents before they occur. Specific skill sets developed in the class are: Analysis of traffic collision and injury data; Analysis of collision risk in a road network (network screening); Identifying crash causal factors; Identifying and evaluating countermeasures; Principles of Road Safety Management; What is the Road safety Audit procedure, and what are aims and objectives, roles and responsibility; history of road safety audit, road safety audit and design standards, road safety audit tasks, various stages of safety audits; common identifiable problems; How to structure a road safety audit report, identify common problems; and case studies and site visits; what to look for on site visits.

The course explores the fundamentals and role of road safety engineering theory and practice. An appreciation of the design of traffic elements on the road network and a rigorous detective approach to investigating road crash data are developed. Participants learn applied skills to find road crash data and analyze it to determine the nature and extent of road crash problems at any given site. An ability to translate road crash data into meaningful information, determine counter measure options from thorough analysis of information and prioritize and evaluate counter measure implementation programs is cultivated. Students become aware of key issues in road safety policy, techniques for accident analysis, and prevention and road safety audit procedures. Other topics include the Highway Safety Manual, screening methods for identifying high collection concentrations, and proactive improvements to traffic safety.

The course examines principles of engineering and behavioral science relevant to preventing traffic collisions and subsequent injury. Human behavior, vehicle design, and roadway design are considered as interacting approaches to preventing traffic crashes and injuries. Safety of vulnerable road users (primarily pedestrians and bicyclists) is covered extensively.

The course introduces the principles of probability and statistics and their applications in engineering. Topics include the relationship between probability and statistics; random variables; probability distributions; mathematical expectation; random sampling; estimation; tests of hypotheses, and regression analysis. 

This course gives a general introduction to water resources, how these are linked the hydrological processes, and how engineering plays a role in the management of water resources. It covers the hydrologic cycle of water as a whole and its specific components including: geophysical flows of water throughout the environment, dynamics of precipitation formations, transformations into runoff, reservoir and lake dynamics, stream flow discharge, surface runoff assessment, calculation of peak flows, the hydrograph theory, ground water flows, aquifers dynamics, concept of water quality and water treatment methods and units. The topics mentioned will be covered in both qualitative and quantitative aspects. Use will be made of essential concepts of energy, mass and momentum conservation. 

This course introduces students to the fundamental theory of the finite-element method (FEM) as a general tool for numerically solving differential equations for a wide range of engineering problems, with special focus on solid and structural mechanics. The course covers the following topics: approximation, weighted residuals and Rayleigh-Ritz methods; finite-element formulation for solids; continuum elements; structural elements; material non-linearity; geometric non-linearity; heat transfer problems and thermal stress analysis; and transient problems.