Intelligent civil infrastructures engineering

L’insegnamento contribuisce agli obiettivi formativi del Corso di Laurea Magistrale in Ingegneria Civile e in Environmental Engineering fornendo strumenti statistici e metodologici avanzati per l’analisi di dati complessi e per l’applicazione di modelli di Machine Learning supervisionato in contesti ingegneristici. Il corso concorre allo sviluppo di competenze trasversali utili all’interpretazione, alla modellazione e al supporto decisionale in problemi complessi dell’ingegneria civile e ambientale, in coerenza con il paradigma della data-driven innovation.

Conoscenza e capacità di comprensione

Al termine del corso, studentesse e studenti conosceranno i principali metodi di Big Data Analytics e Machine Learning supervisionato per problemi di regressione e classificazione. Acquisiranno conoscenze relative alla gestione di dati complessi, alla validazione dei modelli, alla valutazione delle prestazioni predittive e all’interpretazione dei risultati in applicazioni di interesse per l’ingegneria civile e ambientale.

Capacità di applicare conoscenza e comprensione

Studentesse e studenti saranno in grado di impostare correttamente un percorso di analisi di dati complessi, dalla preparazione del dataset alla scelta del modello, fino alla valutazione e interpretazione dei risultati. Sapranno applicare, anche mediante software statistici, metodi avanzati di Machine Learning per problemi di regressione e classificazione riferiti a fenomeni e sistemi di interesse ingegneristico.

Autonomia di giudizio

Studentesse e studenti svilupperanno la capacità di valutare criticamente la qualità dei dati, l’appropriatezza delle tecniche utilizzate, l’affidabilità dei modelli e la correttezza delle interpretazioni. Saranno in grado di confrontare approcci alternativi di analisi e di leggere criticamente risultati ottenuti direttamente o riportati in pubblicazioni scientifiche e applicazioni tecniche.

Abilità comunicative

Studentesse e studenti saranno in grado di comunicare in modo chiaro ed efficace i risultati delle analisi attraverso tabelle, grafici, indicatori sintetici e commenti testuali, utilizzando un linguaggio tecnico-scientifico appropriato e adeguato a contesti ingegneristici e interdisciplinari.

Capacità di apprendimento

Il corso fornirà strumenti metodologici e operativi utili per approfondire autonomamente tecniche di Big Data Analytics, Statistical Learning e Machine Learning, anche in relazione all’evoluzione tecnologica e applicativa dei sistemi complessi nell’ingegneria civile e ambientale.

Richiami di statistica e introduzione al Machine Learning

Breve revisione dei principali concetti e metodi della statistica descrittiva e inferenziale. Introduzione ai concetti fondamentali di Statistical Learning e Machine Learning per problemi di regressione e classificazione in contesti supervisionati, con particolare attenzione all’analisi di dati complessi e high-dimensional.

Modelli di regressione e tecniche di validazione

Estensioni dei modelli di regressione lineare: gestione di dati di natura diversa e di interazioni, regressione polinomiale e procedure di subset selection. Tecniche di ricampionamento e validazione dei modelli: validation set approach, k-fold cross-validation e bootstrap. Tecniche di regolarizzazione dei modelli predittivi: ridge regression e lasso regression.

Metodi di Machine Learning per regressione e classificazione

Regressione logistica per problemi di classificazione. Alberi decisionali per regressione e classificazione. Tecniche ensemble: bagging e Random Forests. Support Vector Classifiers e Support Vector Machines.

Laboratori e applicazioni di data analytics

Il corso è orientato all’analisi pratica di dati complessi mediante software di data analytics e Machine Learning, con applicazioni ed esercitazioni sviluppate principalmente mediante Minitab e KNIME.

È consigliato il possesso di conoscenze di base di statistica e probabilità, utili per la comprensione dei principali metodi di analisi dei dati e di Machine Learning trattati nel corso.

Il corso si articola in lezioni frontali, presentazione e discussione di casi studio, esercitazioni applicative e laboratori dedicati all’utilizzo di software statistici.

Le lezioni frontali sono dedicate all’apprendimento dei principali contenuti teorici e metodologici relativi a Statistical Learning, Machine Learning supervisionato e analisi di dati complessi.

I casi studio, riferiti a contesti applicativi dell’ingegneria civile e ambientale, sono proposti per sviluppare capacità critiche e interpretative nell’analisi di dati reali e per evidenziare il collegamento tra metodi statistici, modelli predittivi e applicazioni ingegneristiche.

L’esercitazione intermedia è finalizzata a consolidare le competenze acquisite e a supportare la preparazione alla prova finale.

I laboratori sono indirizzati all’utilizzo di software statistici e strumenti di data analytics per esemplificare la corretta impostazione, gestione e interpretazione di percorsi di analisi di dati complessi.

Studentesse e studenti con disabilità, DSA, BES e altre condizioni di salute, possono contattare l'Ufficio Servizi agli studenti - Settore Inclusione per ricevere maggiori informazioni sulle opportunità di fruizione della didattica con specifici supporti e strumenti.

La valutazione della prova scritta terrà conto dei seguenti criteri:

• correttezza delle risposte fornite ai quesiti teorici e applicativi;
• completezza nello svolgimento delle analisi richieste;
• correttezza nell’applicazione delle metodologie di data analytics e Machine Learning;
• capacità di interpretare e argomentare criticamente i risultati ottenuti;
• capacità di impostare correttamente workflow di analisi di dati complessi.

L’eventuale elaborato sviluppato mediante software statistici sarà valutato considerando:

• appropriatezza delle tecniche selezionate per l’analisi dei dati;
• correttezza e coerenza della sequenza delle analisi condotte;
• capacità di gestione, elaborazione e interpretazione di dataset complessi;
• chiarezza espositiva e capacità di sintesi dei risultati;
• qualità dell’interpretazione critica dei risultati ottenuti.

La valutazione finale sarà basata principalmente sulla prova scritta; l’elaborato opzionale concorrerà all’integrazione della valutazione complessiva secondo le modalità comunicate dalla docente.

James G., Witten D., Hastie T., Tibshirani R., Taylor J., An Introduction to Statistical Learning: with Applications in Python, Springer, Cham, 2023. ISBN: 978-3-031-38746-3. Note: CONSIGLIATO.

Nelle sue diverse articolazioni il corso ha le seguenti conoscenze ed abilità attese: 1.Conoscenza del funzionamento degli strumenti di BIM Authoring relativi a tutte le discipline e i sistemi costituenti l’organismo edilizio (architettura - strutture - impianti); 2.Conoscenza delle metodologie di verifica e coordinamento dei modelli BIM e pianificazione dei processi di risoluzione delle interferenze/incoerenze di progetto o monitorate in fase realizzativa (clash detection - model checking); 3.Conoscenza delle tecniche di BIM Management e sistemi di codifica e standardizzazione per l’implementazione di reti di modelli interoperabili; 4.Conoscenza delle tecniche e degli strumenti di 4D Modeling per la pianificazione, la visualizzazione grafica e il monitoraggio del processo realizzativo; 5.Conoscenza delle tecniche e degli strumenti di 5D Modeling per la quantificazione economica dei progetti e per la consuntivazione delle opere; 6.Coordinamento e monitoraggio dei processi realizzativi attraverso applicazioni digitali.

I contenuti si articolano sui seguenti argomenti: Introduzione al BIM: Discipline, standard, normative e best practices. Tecniche di modellazione informativa e parametrica in ambito architettonico , strutturale, impiantistico. (BIM Authoring) Tecniche di coordinamento dei modelli BIM: analisi delle interferenze e gestione dei processi di risoluzione delle stesse Tecniche di BIM Management per la gestione di progetti complessi. Sistemi di codifica e standardizzazione per l’implementazione di reti di modelli interoperabili. La pianificazione 4D e la quantificazione delle opere attraverso strumenti BIM: conoscenza dei database, tecniche di sviluppo e software preposti. Coordinamento e monitoraggio dei processi realizzativi attraverso applicazioni digitali, Quantificazione economica dei progetti e procedure di quantity take off da modelli BIM.

Le lezioni alterneranno sessioni di formazione all'uso degli strumenti digitali di modellazione, coordinamento, pianificazione e computazione a lezioni seminariali in cui verranno approfonditi gli aspetti disciplinari connessi allo sviluppo dei suddetti modelli. Le attività laboratoriali verranno affrontate in forma seminariale discutendo con tutti gli studenti le problematiche connesse all'avanzamento del project work e le connesse lezioni apprese.

Le prove pratiche saranno valutate sulla base di una matrice di analisi dei modelli prodotti attraverso la quale si controllerà l'effettiva correttezza delle procedure attuate. La griglia di valutazione verrà trasferita agli studenti in modo tale da supportare il processo di preparazione della prova e conseguente controllo degli eventuali errori.

L'attività laboratoriale verrà analizzata sulla base della correttezza, dell'approfondimento e della chiarezza espositiva delle attività svolte.

appunti delle lezioni

The course aims to:

• provide students with the theoretical fundamentals related to the static and dynamic behavior of spatial structures, both in linear and nonlinear contexts, including through computational modeling activities using commercially available Finite Element Analysis software;
• develop the ability to understand and apply current regulations, in order to correctly interpret results obtained from structural analyses in both static and dynamic contexts;
• strengthen presentation and critical skills, enabling students to clearly and rigorously present the topics covered, analyze them independently, relate them to one another, and apply them to practical cases.

1. General Formulation of the Finite Element Method and application to solid mechanics (static and dynamic):

• Strong and weak formulation of a differential problem
• Variational methods and the principle of virtual work
• Residual methods
• Numerical time integration for dynamic problems.

2. Structural modeling using the Finite Element Method:

• Types of elements (1D, 2D, 3D)
• Construction of the stiffness matrix and assembly procedures
• Convergence criteria and aspects of numerical accuracy.

3. Introduction to nonlinear problems:

• Formulation of the principle of virtual work in the nonlinear context
• Newton-Raphson method.

4. Modeling and analysis in the dynamic field:

• Dynamics of continuous structures (beams, membranes, plates)
• Elastic response spectrum and design spectrum
• Modal analysis
• Dynamic response in the frequency domain
• Structural identification techniques.

Basic knowledge of mathematical analysis and linear algebra;

• Knowledge of continuum mechanics (course of Solid Mechanics);
• Knowledge related to structural design (course of Structural mechanics);
• Knowledge related to numerical calculus (course in Numerical Analysis).

All the fundamental concepts of these prerequisites will be reviewed in class to ensure alignment of basic skills.

The course is offered in a blended format, for a total of 6 ECTS, divided as follows:

• 50% asynchronous online learning (3 ECTS): delivered through video lectures, virtual laboratory sessions, and MOOCs (Massive Open Online Courses), primarily aimed at reinforcing prerequisites and deepening theoretical content relevant to the completion of the course;
• 50% synchronous learning, consisting of:

·       Synchronous online teaching (1 ECTS): conducted through video lectures and virtual laboratories.In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

In-person teaching (2 ECTS): organized into theoretical lectures and laboratory activities.

For both online and in-person teaching, the expected commitment corresponds to two 2-hour sessions per week. These activities are complemented, on a variable schedule, by interactive asynchronous learning, which may include participation in office hours and discussion forums to support continuous study and clarify doubts; reviewing or watching supplementary materials; quizzes, homework, and self-assessment activities monitored by the instructor and tutor.

Inclusion and student support:

In addition to contacting the course instructor, students with disabilities, learning disorders (DSA), special educational needs (BES), or other health conditions can reach out to the Student Services Office – Inclusion Division to receive information on opportunities for accessing the course with specific support and tools.

During the assessment activities, the following will be evaluated:

• the level of learning and understanding of the theoretical fundamentals covered in the course;
• the ability to develop appropriate and effective numerical models for conducting structural analyses in both static and dynamic contexts;
• the ability to address and solve structural engineering problems through the informed use of commercially available Finite Element Method software;
• proficiency in language and clarity of presentation, with particular attention to the accurate use of technical terminology in the field.

• Titolo The Finite Element Method for Solid and Structural Mechanics Autori: Zienkiewicz, Olgierd Cecil; Taylor, Robert Lee; Zienkiewicz,Olgierd Cecil, Luogo: Oxford, Anno: 2005, Editore: Elsevier Butterworth Heinemann.
• Titolo: Structural analysis with the finite element method linear statics, Autori: Oñate, Eugenio; Oñate,Eugenio, Luogo: Barcelona [New York, Anno: 2009, Editore: CIMNE Springer.
• Anil K. Chopra, Dynamics of Structures: Theory and Applications to Earthquake Engineering, Fifth Edition in SI Units, Prentice-Hall International Series in Civil Engineering and Engineering Mechanics, Pearson, 2020
• C. Majorana, C. Modena, P. Franchetti, M. Grendene, S. SecStructural analysis with the finite element method linear staticschi, Fondamenti di Dinamica e di Ingegneria Sismica, McGraw - Hill, 2007
• Clough, R.W., Penzien, J., Dynamics of Structures, Mc. Graw-Hill, NY, USA, ISBN 0-07-011392-0, 1975.

Il corso si propone di fornire agli studenti della laurea magistrale in Environmental Engineering le basi matematico-numeriche per la formulazione, lo sviluppo e la messa a punto di modelli numerici per la soluzione di problemi differenziali evolutivi e di conservazione, con particolare riferimento a tecniche come gli elementi finiti, i volumi finiti e un’introduzione all’uso di algoritmi di intelligenza artificiale. 1. Conoscenza e capacità di comprensione Acquisire i principi fondamentali della modellazione matematico-numerica di problemi ambientali di interesse ingegneristico, comprendendo i principali metodi per la simulazione di processi evolutivi e di conservazione, con cenni all'utilizzo di metodologie di Intelligenza Artificiale. 2. Capacità di applicare conoscenza e comprensione Saper scegliere e applicare gli algoritmi numerici più appropriati per la soluzione di problemi tipici dell'ingegneria ambientale, utilizzando strumenti di calcolo scientifico e valutandone accuratezza, efficienza e stabilità. 3. Autonomia di giudizio Essere in grado di analizzare criticamente i risultati ottenuti, riconoscendo le principali fonti di errore numerico e valutando l'affidabilità e i limiti delle soluzioni calcolate. 4. Abilità comunicative Saper descrivere con chiarezza i metodi numerici impiegati, interpretare i risultati ottenuti e comunicarli efficacemente mediante relazioni tecniche, grafici e strumenti informatici. 5. Capacità di apprendimento Sviluppare le competenze necessarie per approfondire e implementare autonomamente metodi numerici più avanzati e per utilizzare nuovi strumenti di calcolo scientifico in applicazioni dell'ingegneria ambientale.

Introduzione. Problemi non-lineari: metodo di Newton e Newton Inesatto, cenni a tecniche di ottimizzazione. Interpolazione e approssimazione di dati: interpolazione di Lagrange multi-dimensionale, approssimazione ai minimi quadrati, addestramento di reti neurali e cenni all’intelligenza artificiale. Soluzione numerica a equazioni evolutive: problemi ai valori iniziali, differenze finite, metodi di Newmark e Runge-Kutta, problemi di stabilità. Soluzione numerica a equazioni di conservazione: problemi ai valori iniziali e al contorno, metodi di Galerkin e Petrov-Galerkin, Elementi Finiti e Volumi Finiti. Applicazioni: modellazione di gas reali, fitting numerico di dati sperimentali, problemi evolutivi di gestione sostenibile delle risorse, problemi di flusso in acquiferi.

Lezioni frontali. Esercitazioni al calcolatore.

Correttezza e qualità dei progetti. Conoscenza e comprensione degli argomenti svolti a lezione. Capacità di rielaborazione critica dei concetti acquisiti.

• Titolo: Lezioni di Metodi Numerici per l'Ingegneria. Terza Edizione., Autori: Giuseppe Gambolati, Massimiliano Ferronato, Luogo: Padova, Anno: 2022, Editore: Libreria Progetto, Note: --.

1. Conoscenza e comprensione “Lo studente che abbia ottemperato agli obiettivi del corso conoscerà i principali aspetti idraulici e normativi relativi alle intersezioni fra il reticolo stradale e quello idrografico (ponti e tombini), al drenaggio delle sedi stradali e delle piattaforme dei ponti” 2. Capacità di applicare conoscenza e comprensione “Lo studente sarà in grado sviluppare gli aspetti principali della progettazione idraulica degli attraversamenti fluviali mediante l'uso di metodi, strumenti e tecniche aggiornate " 3. Autonomia di giudizio "Lo studente svilupperà la capacità di valutare criticamente i diversi aspetti che concorrono alla progettazione idraulica delle opere di attraversamento di fiumi e canali in modo autonomo" 4. Abilità comunicative "Lo studente sarà in grado di dare una descrizione dell’attività progettuale sia tramite relazioni tecniche e tavole grafiche adeguate sia oralmente" 5. Capacità di apprendimento "Lo studente acquisirà metodi di studio e analisi che gli permetteranno di approfondire autonomamente la materia"

Aspetti normativi. Cenni di idrologia (precipitazioni brevi e intense, calcolo delle portate in piccoli bacini). Elementi di idraulica fluviale (moto uniforme e permanente in fiumi e canali, trasporto solido e detriti galleggianti). Raccolta ed allontanamento delle acque dalle strade (caditoie, cunette e fossi di guardia per strade in rilevato, in trincea e a mezzacosta; stazioni di sollevamento; gestione degli aspetti qualitativi). I sottopassi. Raccolta e allontanamento delle acque dai ponti. Problemi idraulici dei ponti (franco, rigurgito e fenomeni erosivi localizzati indotti da pile e spalle in alveo) e misure mitigatorie. Tombini e guadi. Opere provvisionali in alveo.

Laurea triennale in ingegneria civile o ambientale

studio individuale e sviluppo di esercizi – lezioni frontali sviluppate anche con l’uso di audiovisivi - attività di laboratorio

Il voto finale è la media fra le valutazioni su teoria ed esercitazioni

Da Deppo L., Salandin P. (2026). Sistemazione dei corsi d'acqua, XII Ed., Esculapio, BO.

The course aims to provide students with advanced knowledge and operational skills in managing both one-off activities (design, construction) and repetitive operations (maintenance, service delivery) in civil infrastructures.

During the course, students are expected to:

• Gain foundational insights into service management, focusing on customer relationships, capacity planning, and the digital transformation of infrastructure services.
• Master the principles, approaches, methodologies, and tools for effective project management, from planning and scheduling to control and risk mitigation.
• Enhance digital literacy by integrating advanced tools into project and service management practices.
• Gain foundational insights into the use of artificial intelligence in the management of customer relationship management.

Prepare to coordinate multidisciplinary projects and service operations within complex and dynamic civil infrastructure environments.

By the end of the course, students will have enhanced:

• Independent judgment and critical evaluation skills, through case study analysis, project development, and group work on assigned tasks.
• Problem-solving, decision-making, and critical thinking abilities, essential for managing projects.
• Soft skills. The course will make extensive use of active learning and teaching methods, aimed at developing students’ ability to interact with instructors and peers, stimulating curiosity and critical thinking, fostering teamwork, and enhancing communication effectiveness.

Module 1 (6 ECTS) – Managing Projects in the Civil Infrastructure Context:

• Introduction to project and project management vs operations management.
• Principles and lifecycle of project management, including the role of the project manager. Project management approaches and standards (e.g. PMBOK® Guide, ISO 21500, ISI-PM, PRINCE2).
• Project planning: stakeholder analysis, objective setting, scheduling (Gantt charts, network diagrams), resource allocation and budgeting.
• Project control systems: progress tracking, Earned Value Management, and KPI monitoring.
• Advanced topics: project risk management, multi-project coordination, project management and sustainability, and the balance between predictive and adaptive approaches.
• Practical applications through case studies, workshops, and hands-on sessions with PM software.

Module 2 (3 ECTS) – Managing Services based on Intelligent Civil Infrastructures:

• Basics of service operations management.
• Fundamentals of service management in infrastructures.
• Examination of different service processes, customer-supplier relationships, and CRM practices.
• Capacity management and the role of digital technologies in transforming service delivery.
• Artificial intelligence in the management of customer relationships management.

N/A

Teaching methods include synchronous and asynchronous lectures, online meetings via videoconferencing platforms (Zoom), and online learning activities delivered through the Moodle platform.

A significant portion of the course will consist of lectures incorporating active learning techniques, aimed at promoting the effective understanding and assimilation of course content.

These activities may include:

• Group work, exercises, and individual or group discussions on proposed problems.
• Analysis of real or realistic case studies.
• Guided exercises.
• Interaction with the instructor through various means, such as group assignments and discussion of the outcomes of assigned learning activities.
• Peer feedback and peer assessment
• Guest lectures and corporate testimonials.
• Practical sessions in computer labs.

About 2/3 of the total course hours will be delivered through online teaching modalities (both synchronous and asynchronous), in person lessons are provided at the end of the course.

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

In addition to contacting the course instructor, students with disabilities, specific learning disorders (SLD), special educational needs (SEN), or other health conditions may reach out to the Student Services Office –Inclusion Unit

to receive further information about available tools and support for accessible learning.

The evaluation of students’ knowledge and skills will be based on the following criteria:

• Mastery of the models and techniques presented during the course, as well as their practical implications.
• Ability to independently address conceptual problems discussed in the course, and to apply and justify appropriate practical approaches for their resolution.
• Clarity and appropriateness of expression, including correct use of language and terminology in formulating arguments and responses in the exam.

Nicholas, J. M., & Steyn, H. (2020). Project management for engineering, business and technology. 7th edition. Routledge. ISBN 9781032665887.

The course aims to provide students with special multidisciplinary skills for the analysis and critical evaluation of infrastructure design, specially through a solid training in:

• knowledge of the multidisciplinary topics: rehabilitation, maintenance of existing infrastructures;
• knowledge of design phases for maintenance;
• capability to comprehend the functional and criticality design of infrastructures in relation to the requirements and performance issues;
• knowledge of design phases also through digital, smart and innovative tools.

The contents of the course are aimed at understanding the system of infrastructures in their constructive, functional and technological dimensions, with specific focus on:

• evaluations of construction and technological solutions for infrastructures;
• analysis of guidelines for the evaluation of the principal solutions for renovation and maintenance;
• knowledge of technological systems and construction techniques.

Basic knowledge of Architectural engineering and building construction.

50% asynchronous (Moodle): MOOC videos, quizzes, exercises.

50% synchronous (Zoom): live lessons, exercise reviews, Q&A.

Flipped classroom, project work, peer feedback, case studies. Balanced weekly calendar for steady progress.

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

Use of generative AI is allowed during preparation but not during assessments. Final evaluation is based on independent work.

The course ensures accessibility and inclusive practices for students with Specific Learning Disorders (SLD), Special Educational Needs (SEN) and other health conditions. Please contact the lectures.

In addition, the students can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

In general, the following skills will be assessed and taken into account:

• analytical ability;
• technical accuracy;
• clarity of written reports and project;
• mastery of theoretical concepts.

To be defined during the lessons. 

By the end of the course, students will be able to:

• Analyze steady and quasi-steady flow conditions in rivers and pressurized pipeline systems, with an overview of transient phenomena
• Evaluate water availability and flow propagation in river systems
• Understand the principles of sediment transport and riverbed morphodynamics
• Apply hydraulic measurement techniques for river and network monitoring
• Interpret monitoring data
• Understand regulations and technical standards for water infrastructures
• Develop numerical models for steady and quasi-steady flow analysis in rivers and water distribution networks

• Structure of water supply systems: water sources and intake works, adduction systems, storage tanks, pumping stations, distribution networks, materials, and typical components
• Steady and quasi-steady flow analysis in water distribution networks for operational and management purposes
• Numerical modeling of water distribution systems for demand allocation, pressure management, leakage analysis, and operational optimization
• Regulations and technical standards for water supply networks and river infrastructures
• Introduction to Water Safety Plans (WSPs): risk assessment, monitoring, operational control, and implementation in water supply systems
• Water propagation in rivers under steady and unsteady flow conditions
• Principles of sediment transport, bedforms, and river morphodynamics
• Interaction between flood waves, sediment transport, and built structures
• Hydraulic measurements for river and network monitoring: water level, pressure, velocity, and discharge
• Operating principles of instrumentation and interpretation of measured data outputs

Students are expected to have basic knowledge of:

• Mathematics (calculus and differential equations)
• Fluid mechanics and hydraulics

Previous coursework in river engineering, water resources engineering or pipeline hydraulics is recommended but not mandatory.

The course includes:

• Lectures supported by slides and technical examples
• Problem-solving sessions and numerical exercises
• Analysis of real-world case studies
• Introduction to professional numerical modeling tools
• Interpretation of monitoring data from rivers and water networks
• Technical visits and field monitoring activities.

Teaching methods combine traditional lectures with applied problem-based learning approaches, blended/flipped classroom/active learning. Asynchronous lessons (Mooc), and meetings (Zoom) and online activities in Unipd Moodle platform. In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

Regarding AI, we encourage its active and critical use, both as an object of study and as a teaching/learning tool. The use of generative AI tools to draft assignments or to replace one’s own reasoning is not allowed.

In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), and other health conditions can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

Evaluation will be based on:

• Knowledge of theoretical concepts in hydraulics and water infrastructure management
• Ability to correctly apply analytical and numerical methods
• Quality of problem-solving and critical reasoning
• Correct interpretation of monitoring data and modeling results
• Clarity, rigor, and technical accuracy in written and oral communication

Final grades will follow the institutional grading system.

Indicative references include:

• Julien, P.Y., River Mechanics
• Mays, L.W. (Ed.), Water Distribution Systems Handbook.

Additional references will be provided during the course.

At the end of the course, students will have acquired advanced, internationally applicable competences to operate as skilled professionals in the monitoring, maintenance, and management of smart transport infrastructures

(roads, railways, and airports). Specifically, they will be able to:

 Frame transport-infrastructure assets and performance needs (structural, functional, safety, resilience, and serviceability) and relate them to lifecycle management goals.

 Select and justify monitoring approaches for different asset types, combining in-situ sensing, non-destructive testing, and remote/vehicle-based measurements within practical constraints (cost, accessibility, reliability).

 Design a data-driven monitoring workflow accounting for uncertainty, missing data, and real-world operational conditions.

 Interpret multi-source condition/performance data to diagnose distress and degradation mechanisms and to derive actionable condition indicators and performance thresholds.

 Apply predictive and decision-support concepts (deterioration modelling, risk/reliability, and scenario analysis) to anticipate needs and prioritize interventions.

 Develop maintenance and rehabilitation strategies that balance performance, user disruption, safety, sustainability, and whole-life cost across roads, railways, and airports.

 Use asset-management principles and key standards-oriented practices (e.g., structured inventories, KPIs, and maintenance planning) to support transparent, auditable decisions.

Communicate technical findings and recommendations clearly to diverse stakeholders through concise technical reporting and data-driven storytelling suitable for an international context.

The course introduces concepts, methods, and tools for the monitoring, maintenance, and management of smart transport infrastructures (roads, railways and airports).

Main topics include:

• Transport infrastructure as an asset system
• Monitoring technologies and measurement strategies
• Data and information management
• Condition assessment and diagnostics
• Predictive approaches and decision support
• Maintenance and intervention strategies

None.

The course adopts an integrated approach that combines conceptual foundations with applied, practice-oriented learning in a fully online environment.

Activities include:

• Synchronous lectures (Zoom)
• Asynchronous learning units (Moodle)
• Interactive online exercises
• Case-study-based learning
• Guided project work
• Guest seminars/webinars
• Online discussion

 In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

Teaching methods are designed to promote active participation, continuous feedback, and critical thinking, enabling students to connect monitoring data and engineering judgement to practical maintenance and management decisions in an international context.

USE OF GENERATIVE AI

Generative AI can be used by both instructors and students to enhance the learning experience (e.g., chatbots, tutors, virtual assistants, simulations, data analysis) and improve accessibility (e.g., text-to-speech, machine translation), thus fostering inclusion.

It can support the development of technical skills, teamwork, content reworking, brainstorming, and peer review. It can also help explore, rework, and apply learning materials, consolidate knowledge, and encourage reflection on the work done, through discipline-specific chatbots, whether provided by the instructor or developed during class activities.

DISABILITIES

In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), and other health conditions can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

The evaluation (oral discussion of the assigned project) will be based on:

• In-depth knowledge of the course topics for roads, railways, and airports.
• Quality and rigor of the project work.
• Ability to critically elaborate on the proposed solutions, discussing limitations, alternatives, and implications for safety, resilience, sustainability, and lifecycle performance.
• Clarity and effectiveness of communication, using appropriate technical and scientific language and a well-structured presentation of objectives, workflow, results, and recommendations.
• Relevance and coherence of argumentation during the discussion, including the ability to answer questions consistently and to justify decisions in an internationally applicable context.

Additional readings will be provided during the course, connected to the assigned project.

The target skills and knowledge of Smart transportation systems (1) and infrastructures (2) are:

(1-a) describing the main components and functionalities of a traffic monitoring system;

(1-b) selecting and calculating quantitative indicators to evaluate mobility management interventions;

(2-a) describing transportation infrastructures composition, function and organization;

(2-b) acquiring knowledge on methodologies for maintenance and management of smart and traditional transportation infrastructures.

Module 1 - Transportation Systems

1.1 Principles of traffic monitoring system and innovative technologies for analyzing and evaluating mobility solutions;

1.2 Main concepts and indicators for designing and assessing mobility management interventions;

1.3 Advanced services and policies for planning an efficient and sustainable mobility system;

1.4 Innovative technologies and applications for managing mobility and transport;

Module 2 - Transportation Infrastructures

2.1 Functional and structural classification, standards;

2.2 Infrastructure evolution and digital adaptation;

2.3 Maintenance: traditional vs. smart, predictive methods;

2.4 Safety management infrastructures.

Basic knowledge of road, railway and airport construction and fundamentals of transportation systems.

Students lacking this background must complete preparatory teaching materials (e.g. recorded MOOC) provided by the lecturers.

50% asynchronous (Moodle): MOOC videos, quizzes, exercises.

50% synchronous (Zoom): live lessons, exercise reviews, Q&A.

Flipped classroom, project work, peer feedback, case studies.

Balanced weekly calendar for steady progress

Use of generative AI is allowed during preparation but not during assessments. Final evaluation is based on independent work.

The course ensures accessibility and inclusive practices for students with Specific Learning Disorders (SLD), Special Educational Needs (SEN) and other health conditions. Please contact the lectures. In addition, the students can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

In general, the following skills will be assessed and taken into account:

• analytical ability,
• technical accuracy,
•  clarity of written reports,
• correctness of calculations,
•  mastery of theoretical concepts.

Particular attention will be given to the ability to apply theoretical concepts in practical exercises and to demonstrate critical thinking when addressing a problem.

• Traffic Monitoring Guide. U.S. Department of Transportation Federal Highway Administration, Washington, D.C., 2022.
• The Handbook of Highway Engineering, T.F. Fwa, CRC Press, Taylor & Francis Group, 2006.
• Highway Engineering - Pavements, materials and control of quality. A. Nikolaides, CRC Press, Taylor & Francis Group, 2015.
• Highway Maintenance Handbook, K. Atkinson, Thomas Telford, 1997.

The course aims to provide students with advanced knowledge and operational skills for the digitalization and management of complex civil infrastructures throughout their entire life cycle. In particular, at the end of the course the student will be able to:

• Develop skills in the configurative genesis of surfaces of complex civil structures, involving activities of design, construction, maintenance, management.

• Learn to apply appropriate supervision tools based on the level of digital survey, interoperable modelling (Building Information Modeling – BIM), georeferencing systems (GIS).

• Acquire knowledge of methodologies to understand and adopt Scan-to-BIM.

• Mastering foundational digital transformation of infrastructure analized.

• Master the principles, approaches, methodologies, and tools for a correct digitalization of civil infrastructures.

• Coordinate multidisciplinary projects within complex and dynamic civil infrastructure environments.

• Develop transversal skills through experiential learning (on-site surveys) and teamwork (Scan to BIM and GIS to BIM operations).

• Embrace the mindset of considering civil infrastructures along the entire live from conception, through design, construction, and managing, revamping, dismantle.

• Being able to recognise differences among one-off activities (e.g. design, construction) and repetitive operations (e.g. maintenance, service delivery) in civil infrastructures, while appreciating their impacts on organizational and managerial activities.

• Understand the dual nature of outputs—physical infrastructures and services—and address the challenges inherent in each.

• Being able to recognize appropriateness of different management tools based on the level of customization, modularization, and standardization of the infrastructure and repetitiveness of activities.

Module 1 (3 ECTS) – Life-cycle perspectives of civil infrastructures

• Live-cycle perspectives of civil infrastructures.

• Civil infrastructures as socio-technical systems.
• Differentiation between artefacts (physical infrastructures) and services (usage and maintenance of infrastructures). Balancing customization and standardization of artefacts and services. Innovation and operations activities.
• Distinction between project-based (one-off) versus repetitive activities.
• Fundamental cost measurement: variable/fixed costs, direct/indirect costs, job costing, and cost centers.
• Basics of repetitive operations management: bill of materials, production cycle, and material requirements planning in construction.

Module 2 (3 ECTS) – Infrastructure digital survey :

• Collection and documentation of metric information.

• Survey operations: photogrammetry and laser scanning procedures.
• Point cloud extraction with application in actual case studies.

Module 3 (3 ECTS) – Interoperable 3D modelling of configurative surfaces:

• Principles of interoperable digitalization of the analysed case study.

• BIM for design, management and maintenance of civil infrastructures.
• The digital deliveries to be implemented in BIM: types and roles.
• GIS to BIM implementation to put in relation the analysed civil infrastructure to the land condition.

No specific prerequisites are required.

Teaching methods include synchronous and asynchronous lectures, weekly online meetings via videoconferencing platforms (Zoom), and online learning activities delivered through the Moodle platform.

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

 In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), and other health conditions can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

Assessment is based on the evaluation of the knowledge and competencies acquired,

the capacity to identify and justify appropriate solutions to practical problems

and the demonstrated mastery of the course contents.

Titolo: Architectural geometry, Autori: Pottmann, Helmut; Reimer, Eva; Kasiz-Hitz, Elisabeth; Bentley, Daril; Pottmann,Helmut, Luogo: Exton (PA), Anno: 2007, Editore: Bentley Institute Press.

Titolo: Autodesk Revit 2020 per l'architettura guida completa per la progettazione BIM, Autori: Pozzoli, Simone; Bonazza, Marco; Villa, Werner Stefano; Pozzoli,Simone, Luogo: Milano, Anno: 2019, Editore: Tecniche nuove, Note: for Revit 2025 see the following web-page: https://help.autodesk.com/view/RVT/2025/ENU/?guid=RevitReleaseNotes_2025release_html.

Titolo: death of drawing architecture in the age of simulation, Autori: Scheer, David Ross; Scheer,David Ross, Luogo: London, Anno: 2014, Editore: Routledge.

Il corso introduce al tema della mobilità sostenibile, illustrando gli approcci e gli indicatori impiegati per la valutazione della sostenibilità di soluzioni e politiche per la mobilità, e fornendo elementi utili per la comprensione delle azioni nell’ambito dei processi di pianificazione sostenibile dei sistemi di trasporto.

1.     Conoscenza e comprensione

Al termine dell’insegnamento, la studentessa/lo studente conoscerà:

• I principali interventi per la mobilità sostenibile
• Gli strumenti per la loro pianificazione
• Le metodologie per la valutazione dei loro impatti ambientali, sociali ed economici

2.     Capacità di applicare conoscenza e comprensione

La studentessa/Lo studente sarà in grado di:

• Selezionare gli indicatori di sostenibilità per la valutazione degli interventi di mobilità sostenibile
• Modellare e stimare gli effetti delle misure di mobilità sostenibile, considerando l’interazione tra le attività antropiche e i sistemi naturali
• Utilizzare strumenti Geographic Information System (GIS) per l’analisi degli indicatori di sostenibilità
• Partecipare alla definizione di soluzioni/politiche adeguate alla mitigazione degli impatti ambientali dei sistemi di mobilità

3.     Autonomia di giudizio

La studentessa/Lo studente svilupperà la capacità di interpretare gli indicatori di sostenibilità degli interventi di mobilità e quindi di valutarne in maniera critica gli impatti ambientali, sociali ed economici, nell’ambito dei piani di programmazione territoriale e delle procedure di valutazione ambientale.

4.     Abilità comunicative

La studentessa/Lo studente sarà in grado di comunicare i risultati del proprio lavoro, interagendo in modo sinergico con i professionisti del settore dell’ingegneria dei trasporti e con le strutture tecniche delle pubbliche amministrazioni, nell’ambito, ad esempio, di attività di valutazione degli interventi sul sistema territorio/trasporti (Valutazione di Impatto Ambientale, Piano Urbano per la Mobilità Sostenibile, etc.).

5.     Capacità di apprendimento

La studentessa/Lo studente acquisirà i metodi per poter approfondire e sviluppare in maniera autonoma le procedure per la valutazione del livello di sostenibilità di diversi interventi di mobilità. 

Il tema della mobilità sostenibile verrà trattato considerando il concetto di sostenibilità a diversi livelli: ambientale, economico e sociale. Il corso includerà aspetti teorici, ma anche casi studio di interesse pratico per le attività di pianificazione e valutazione di interventi per la mobilità sostenibile.

ASPETTI TEORICI

• Introduzione ai concetti di sviluppo sostenibile: dalla sostenibilità ambientale, sociale ed economica all’equità, fattibilità e vivibilità
• Panoramica sulle caratteristiche tecniche e funzionali dei sistemi di trasporto urbano: mezzi privati, veicoli elettrici, micromobilità, trasporto pubblico, servizi di sharing, servizi on-demand
• Misure e interventi di mobilità sostenibile: concetti generali e casi studio
• Normativa europea, e strumenti di pianificazione, gestione e valutazione di soluzioni di mobilità sostenibile: Piano Urbano per la Mobilità Sostenibile, Piano d’Azione per l’Energia Sostenibile e il Clima, Valutazione d’Impatto Ambientale, la figura del mobility manager e la carbon footprint dei viaggi sistematici nel Piano degli Spostamenti-Casa Lavoro
• Indicatori di sostenibilità ambientale: stima delle emissioni inquinanti, life-cycle assessment di soluzioni di mobilità
• Indicatori di sostenibilità sociale (ad esempio, accessibilità, sicurezza stradale)
• Indicatori di sostenibilità economica degli interventi sul sistema dei trasporti
• Soluzioni innovative per la mobilità sostenibile: attenzione specifica sarà rivolta all’evoluzione del sistema dei trasporti in un’ottica di progressiva introduzione di veicoli a guida automatizzata e sistemi Mobility-as-a-Service.

CASI STUDIO PRATICI (a titolo d’esempio)

• Stima delle emissioni inquinanti da traffico in relazione alla variazione dei volumi di traffico e della composizione veicolare
• Valutazione degli Impatti di soluzioni infrastrutturali: analisi prima-dopo dell’implementazione di specifici interventi
• Sviluppo di piani per la mobilità sostenibile

L’insegnamento prevede:

• Lezioni frontali
• Lavori di gruppo
• Esercitazioni pratiche
• Presentazione di casi studio reali

Metodologie di insegnamento previste:

• Esercitazioni guidate
• Attività interattive in aula
• Active learning
• Problem solving
• Discussione di casi studio reali

L’uso dell’IA è consentito per lo studio individuale ma vietato in sede d’esame.

Studentesse e studenti con disabilità, DSA, BES e altre condizioni di salute, possono contattare l'Ufficio Servizi agli studenti - Settore Inclusione per ricevere maggiori informazioni sulle opportunità di fruizione della didattica con specifici supporti e strumenti.

Prova scritta:

• quesiti a scelta multipla: +1 punto per ogni risposta corretta; domande aperte: + 3 punti per ogni risposta corretta
• 0 punti per risposta errata o non data
• massimo: 30 punti

Prova orale:

• punteggio variabile da 0 a +3 punti
• i criteri per la valutazione sono la capacità di applicazione della metodologia, la chiarezza nel procedimento impiegato e la capacità di interpretazione dei risultati

La valutazione finale è data dalla somma dei punteggi nelle due prove, subordinata al superamento di entrambe (18/30 per la prova scritta e 1/3 nella prova orale).

1. Corporate Partnership Board, Good to Go? Assessing the Environmental Performance of New Mobility, 2020. https://www.itf-oecd.org/good-go-assessing-environmental-performance-new-mobility
2. European Commission: Directorate-General for Mobility and Transport, CE Delft, Essen, H. v., Wijngaarden, L. v., Sutter, D. et al., Handbook on the external costs of transport – Version 2019 – 1.1, Publications Office, 2020, https://data.europa.eu/doi/10.2832/51388
3. Leonidas Ntziachristos, Zissis Samaras, EMEP/EEA air pollutant emission inventory guidebook. Passenger cars, light commercial trucks, heavy-duty vehicles including buses and motor cycles, 2023. https://www.eea.europa.eu/en/analysis/publications/emep-eea-guidebook-2023
4. Rupprecht Consult - Forschung & Beratung GmbH (editor), Guidelines for Developing and Implementing a Sustainable Urban Mobility Plan, Third Edition, 2025. https://urban-mobility-observatory.transport.ec.europa.eu/sustainable-urban-mobility-plans/guidelines-developing-and-implementing-sustainable-urban-mobility-plan-0_en
5. The Committee of the Transport Access Manual, Transport Access Manual: A Guide for Measuring Connection between People and Places, 2020. https://transportist.org/books-2/books/transport-access-manual-a-guide-for-measuring-connection-between-people-and-places/
6. Todd Litman, Evaluating Transportation Equity Guidance for Incorporating Distributional Impacts in Transport Planning, 2026. https://www.vtpi.org/equity.pdf
7. TRACECA IDEA, Guidelines for Pre-Feasibility of Transport Projects with Exercises and Case Studies, 2008. https://www.trt.it/documenti/Guide-CBA-ENG.pdf

COGNITIVE SKILLS

By the end of the course, students will be able to:

C1. Understand the fundamental concepts and analytical tools for evaluating the functioning of capital markets and capital budgeting decisions.

C2. Assess investment decisions from an economic and financial perspective.

C3. Explain the role of infrastructure contracts, incentives, and regulatory frameworks in shaping investment outcomes.

PRATICAL SKILLS

At the end of the course, students will be able to:

P1. Apply the main tools used in capital market analysis.

P2. Analyse and discuss the evaluation of investment projects.

P3. Support decision-makers in implementing investment strategies and in designing appropriate incentive contracts.

TRANSFERABLE SKILLS

Students will develop:

T1. Critical thinking and problem-solving abilities.

T2. Strong analytical and interpretative skills in applied economic contexts.

MOOC 1

• Introduction and Economic Foundations
• Introduction to the course and key principles of economics
• From national accounts to the fundamentals of capital markets
• Economics of contracts, incentives, and infrastructure governance

MOOC 2

• Investment Evaluation
• Cost of capital and the CAPM model
• Taxation and its effects on investment and corporate financing decisions

N/A

The course will combine:

• Lectures
• Group work activities
•  Active learning and classroom discussion
• Self-assessment exercises

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), and other health conditions can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

Learning will be monitored throughout the course through short in-class and online activities and intermediate tests aimed at supporting students’ progress.

These assessments will mainly serve as self-evaluation tools, allowing students to check their understanding of the key concepts and methods discussed during the lectures. They are not intended to replace the final examination, but rather to provide continuous feedback and reinforce learning during the course.

The Economics of Contracts: A Primer by Bernard Salanie, MIT Press, 2ND edition 2017

(core reference for contracts, incentives, and infrastructure governance).

For complementary topics in corporate finance and investment evaluation:

Brealey, R., Myers, S., & Allen, F. Principles of Corporate Finance. McGraw-Hill.

(reference for cost of capital, CAPM, and capital budgeting).

Introductory material for students with limited economics background:

Mankiw, N.G. Principles of Economics. Cengage Learning.

(background reading on fundamental economic concepts)

Additional articles and policy notes on taxation, national accounts, and infrastructure finance will be provided during the course via Moodle.

The course aims to provide:

• the fundamental concepts of structural behaviour under static and dynamic conditions, according to bridges’ structural typology;
• the skills to correctly interpret and apply guidelines and standards for assessment, management and monitoring of bridge infrastructure;
• the tools to be used as decision-making support and prioritization in the bridge infrastructure safety management process;
• the actions for risk mitigation in bridge infrastructure.

• Basic concepts of structural behaviour according to bridge typologies;
• Analysis of guidelines and standards for bridge management;
• Inspection and classification of bridge infrastructures;
• Advanced techniques for Smart Structural Health Monitoring of bridges;
• Decision-making processes for bridge infrastructure safety and management;
• Risk mitigation interventions and special procedures for critical bridges.

Basic knowledge of structural engineering and structural mechanics.

50% asynchronous (Moodle): MOOC videos, quizzes, exercises.

50% synchronous (Zoom): live lessons, exercise reviews, Q&A.

Flipped classroom, project work, peer feedback, case studies.

Balanced weekly calendar for steady progress

Use of generative AI is allowed during preparation but not during assessments. Final evaluation is based on independent work.

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

The course ensures accessibility and inclusive practices for students with Specific Learning Disorders (SLD), Special Educational Needs (SEN) and other health conditions. Please contact the lectures.

In addition, the students can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

In general, the following skills will be assessed and taken into account:

• analytical ability,
•  technical accuracy,
• clarity of written reports,
• correctness of calculations,
• mastery of theoretical concepts.

• How to read bridges - Edward Denison & Ian Stewart - Herbert Press London.
• IABSE SED (green books):

1. Introduction to Safety and Reliability of structures - Jorg Schneider.
2. Use of Fibre Reiforced Polymers in Bridge Construction.
3. Use and application of High Performance steels for steel structures.
4. Ship collision with bridges.
5. Design of robustness.
6. Cable vibration in cable stayed bridges.
7. Case studies of rehabilitation, repair retrofitting and strengthening of structures.

• IABSE blu books: Case histories.
• Dinamic of railway bridges - Ladislav Fryba - ed. Thomas Telford.
• Bridge design, construction and maintenance - ICE -Ed. Thomas Telford.
• Displacement based seismic design of structures - Priestley Calvi Kowalsky - IUSS Press.
• Cable supported bridges concept and design - Gimsing & Georgakis ed. Wiley and sons.
• Cable stayed bridges - Svensson ed. Wiley Blackwell.
• The world of footbridges - Idelberger - Ed. Ernst e Sohn.
• Prestressed concrete bridges - Hewson Ed. Telford.
• Arch bridges - Melbourne Ed. Telford.
• Failed Bridges - Scheer -Ed. Ernst e Sohn.

The course aims to provide advanced theoretical and applied knowledge in Multi-Criteria Decision Aiding (MCDA) and Decision Support Systems (DSS) for civil infrastructure engineering,

with specific reference to intelligent and sustainable infrastructure systems.

Knowledge and understanding 

At the end of the course, students will have acquired:

• knowledge of decision theory applied to civil infrastructures; 
• understanding of the principles, assumptions and limitations of MCDA methods; 
•  knowledge of the structure and components of Decision Support Systems; 
• understanding of how technical, economic, environmental, and social criteria interact in infrastructure decision-making. 

Applying knowledge and understanding 

Students will be able to:

• formulate and structure complex decision problems related to the planning, management and maintenance of civil infrastructures; 
• apply MCDA methods to real or realistic infrastructure case studies; 
• design and interpret decision support frameworks and systems integrating multiple criteria and data sources. 

Making judgements 

Students will be able to: 

• critically evaluate alternatives under multiple, conflicting objectives and stakes; 
• analyze the effects of uncertainty, stakeholders’ preferences, and sensitivity assumptions on decision outcomes;
• structure decision processes and support decision-makers in expressing and analyzing their value systems

Communication skills 

Students will be able to: 

• communicate decision models, assumptions and results using appropriate technical language;
• present analyses and disseminate their results clearly in written works and oral presentations;
• discuss and justify decision-making strategies in a collaborative and interdisciplinary context. 

Learning skills 

Students will develop: 

• the ability to autonomously deepen methodological aspects through self-learning activities;
• skills in organizing individual study and project work in an online learning environment;
• competencies for lifelong learning in the evolving field of decision support for civil infrastructures.

The course covers the principles and methods supporting structured decision-making and decision-aiding in engineering, with specific reference to intelligent civil infrastructures.

It introduces the fundamentals of decision theory and decision-making processes in engineering contexts, focusing on the formulation and structuring of complex decision problems

related to the planning, design, management and maintenance of civil infrastructures. The course provides an overview of multi-criteria approaches for the evaluation of alternative

solutions and investigates the fundamentals of Multi-Criteria Decision Aiding and its application, and the role of multiple and often conflicting criteria in infrastructure-related decisions.

Attention is devoted to preference aggregation and aggregation procedures, and to the interpretation of results.

Further topics include the legitimation, transparency, and robustness of decisions, and sensitivity analysis.

Applications are presented in the context of civil infrastructures and are aimed at addressing choice,

sorting, classification, ranking, and rating problems.

To attend the course profitably, students are expected to possess the following knowledge, skills, and competencies:

• basic mathematical skills, including binary relations, linear algebra, and introductory probability and statistics;
• basic digital skills for data handling, including the use of spreadsheets and familiarity with online learning platforms.

These prerequisites are recommended but not mandatory.

The course is delivered predominantly online and integrates:

• online teaching - synchronous (50%) and asynchronous (50%) lectures- and in presence teaching at the end of the course;
• interactive teaching, including live discussion sessions, guided exercises, case studies and group project work;
• self-learning activities, based on individual study of teaching materials and autonomous development of project tasks.

All activities are supported by the institutional Moodle STEM e-learning platform.

In accordance with current legislation, this course is delivered ensuring that at least 20% of online activities are delivered synchronously.

In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), and other health conditions can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

Learning assessment is based on:

• online quizzes (multiple-choice and open-ended);
• graded exercises and intermediate assignments;
• participation in online discussions, forums and live interactive sessions;
• evaluation of the project work;
• final exam.

Assessment activities are managed through the Moodle STEM e-learning platform, ensuring continuous monitoring of learning progress, with the exception of the final exam.

• Bouyssou, D., Marchant, T., Pirlot, M., & Alexis Tsoukiàs, P. V. (2006). Evaluation and Decision Models with Multiple Criteria: Stepping stones for the analyst (International Series in Operations Research & Management Science, Ed.; 1st ed.). Springer New York, NY. https://doi.org/10.1007/0-387-31099-1
• Bouyssou, D., Marchant, T., Pirlot, M., Perny, P., Tsoukias, A., & Vincke, P. (2000). Evaluation and Decision Models: A Critical Perspective. Springer US. https://books.google.it/books?id=W5s8zQ0vhcUC
• Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation (McGraw-Hill, Ed.).
•  Saaty, T. L. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process. Vol. VI of the AHP Series. RWS publications, Pittsburgh.
• Tsoukiàs, A. (2008). From decision theory to decision aiding methodology. European Journal of Operational Research, 187(1), 138–161. https://doi.org/https://doi.org/10.1016/j.ejor.2007.02.039