Applied geomechanics

The course offers the fundamentals of rock and soil behaviour and the site engineering application for environmental, quarrying, mining, tunnelling purposes and safety issues for excavations as well. The course is based on the characterisation of rock materials and masses, of non conventional geo-materials and on the solution of some typical cases for surface and underground excavations, while the monitoring of natural and artificial structures is presented as a part of the design methodology in mining, tunnelling and environmental geomechanics and technologies. The content of the lessons is intended to provide a useful reliable background for the excavation engineering and it is a link with oriented courses , such as Tunnelling, Rock and soil reinforcing, Mining methods, Underground excavations, Landslides and slope stability; monitoring concepts in the excavation engineering for safe works are also intended as unavoidable requirements.

Data science and machine learning for engineering applications

This course explores how new algorithms in the Data Science (DS), Machine Learning (ML), and Deep Learning (DL) disciplines can help engineering applications become smarter. These emerging disciplines could play a significant role and substantially impact various sectors. Thus, they could lead to new opportunities to develop cutting-edge and unconventional engineering applications. The course focuses on designing and implementing data-driven processes to extract knowledge from data and support decision-making. The course first introduces the data science process, focusing on its main phases. It then provides a theoretical and practical understanding of data mining and machine learning algorithms commonly used to analyze large and heterogeneous data in engineering scenarios. The course also introduces the Python language and state-of-the-art data mining and machine learning libraries to design and develop machine learning-based applications. Many laboratory sessions, based on a learning-by-doing approach, allow experimental activities on all the phases of a standard data science process - data preparation and cleaning, data exploration and characterization, machine learning algorithm selection and tuning, result assessment - using the most widespread open-source tools and libraries.

Applied geomechanics

After the course, the student will be able to: - define and obtain the main geomechanical properties of a rock material, of non conventional geo-materials and of a rock mass as well - carry out a geostructural survey and interpret a geostructural analysis. - recognize the behaviour of geological formations and materials as fundamental for the solution of practical problems in the geoengineering and geoenvironmental field. - evaluate the rock mass quality by following a classification system - evaluate safety factors for stability in basic problems (rock block, embankment, underground wedge) - recognize the role of a support system - manage some basic and worldwide used commercial codes for geomechanics - select parameters for monitoring of geological formations and structures (dumps and slides for example) is offered to the students.

Data science and machine learning for engineering applications

• Knowledge of the main phases characterizing a data science and machine learning process for a real-life engineering application. • Knowledge of the major data mining algorithms for classification, regression, clustering, and association rule mining. • Knowledge of the major machine learning and deep learning algorithms. • Knowledge of the Python language. • Knowledge of the major data mining and machine learning libraries. • Ability to design, implement and evaluate a data science process. • Ability to design, implement and evaluate a machine learning and deep learning algorithm. • Ability to use and tune data mining and machine learning algorithms. • Students will acquire all the above abilities by exploiting state-of-the-art machine learning algorithms in engineering applications.

Applied geomechanics

Basic knowledge of physics, mechanics, statics and applied geology. The mentioned background helps to better and easily understand novelties of the course. Measurements fundamentals are also recommended. Specific seminars are organized for those students who want to equalize lack of pre-requirements.

Data science and machine learning for engineering applications

Basic programming skills.

Applied geomechanics

Rock materials and rock masses. Applications of rock engineering in mining, tunnelling, environmental engineering and civil excavations. Non conventional geo-materials: waste rocks, muds, slurries, tailings, spoil, debris, slags, grout. Fundamentals of elasticity in rock mechanics; stress and strain. Ground formations and basic properties of soils. Geomechanical characterization in lab and in field. Experiences of testing in laboratory. Strength criteria and behavior of rock materials and non conventional geo-materials. Joint properties and related shear strength criteria Geomechanical characterization of natural formations and behaviour of rock masses. Scale effect, difference between continuous and discontinuous media. Induced stress – strain distribution in rock masses (Airy, Kirsch, elasto plastic conditions). Geomechanical classifications and correlations with rock mass properties and technological features (classes of support, unsupported span and self supporting time, excavability). Design methods in rock engineering (analytical, graphical, numerical, empirical). The concept of safety factor. Partial safety factors. Stereographic projections. Kinematics of failure modes in rock masses (translational, ravelling, slabbing, squeezing, rockburst etc) Fundamentals of in situ stress measurement methods Analyses of some basic geomechnical problems for natural conditions and excavations: stability of underground excavation, surface excavation, application of limit equilibrium method, bolting techniques, remedial works, embankments, mining problems, in order to focus and understand the theory. Stability of dumps and landfills in waste rocks and muck. Monitoring issues for mining, environmental, rock and soil engineering. Particular cases: large landslides, morainic cover and debris from glaciers, unexpected and large events in mining and environmental fields. Classworks.

Data science and machine learning for engineering applications

• Data science process: main phases (0.2 cfu) • Data collection, cleaning, transformation and enrichment, and feature engineering (0.3 cfu) • Data mining algorithms: classification and clustering (0.8 cfu) • Machine learning and deep learning algorithms (0.6 cfu) • Introduction to Python and data mining and machine learning libraries (e.g., scikit-learn) (1.0 cfu) • Case study analysis (0.6 cfu) • Design in the lab of data science process and machine learning and deep learning algorithms for engineering applications (2.5 cfu)

Applied geomechanics

Data science and machine learning for engineering applications

Applied geomechanics

The course is based on lessons and worked examples during classworks. Some lab experience will be carried out in order to provide some ability in practical testing operations. Basic numerical modelling of surface - underground - reinforced structures are carried out in lab. Seminars from eminent Authors (also online) are presented to spread different viewpoints and technical communication. If possible, a technical visit is organized on site.

Data science and machine learning for engineering applications

The course includes lectures and practices on the course topics, particularly on data science process design, data preprocessing, and machine learning and deep learning algorithms (3.5 CFU). The course includes laboratory sessions on the data science processes and machine learning algorithms for engineering applications (2.5 CFU). Laboratory sessions allow experimental activities on the most widespread tools and libraries. Students will prepare a written report on a group project assigned during the course and discuss it with the professor.

Applied geomechanics

The presentations of the lessons are loaded on the website and they follow the program. Class notes taken directly during lessons are recommended as fundamental for the student. Reference textbooks are presented (and available in the DIATI library) for details. Among the others: Hoek E. Practical Rock Engineering, Rocscience; Brady B.H.G. Brown E.T. Rock Mechanics for Underground Mining.

Data science and machine learning for engineering applications

Copies of the slides used during the lectures, examples of written exams and exercises, and manuals for the activities in the laboratory will be made available. All teaching material is downloadable from the course website or the teaching Portal. Book (only a few chapters needed) - Tan, Steinbach and Karpatne, Kumar; 'Introduction to data mining', 2 ed., Pearson, 2019 - Bellini, Alessandro and Guidi, Andrea; ‘Python e machine learning’, McGraw-Hill Education, 2022

Applied geomechanics

Slides;

Data science and machine learning for engineering applications

Applied geomechanics

Modalità di esame: Prova scritta (in aula); Prova orale obbligatoria;

Data science and machine learning for engineering applications

Modalità di esame: Elaborato progettuale in gruppo; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;

Applied geomechanics

Exam: Written test; Compulsory oral exam;

Data science and machine learning for engineering applications

Exam: Group project; Computer-based written test in class using POLITO platform;

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Applied geomechanics

During the course period, two written tests will be carried out (half of the course and end of the course) aimed to student self verification (1,5+1,5 h); min score 18, max score 30. No didactical material is permitted during these tests. The official exam during the exam sessions is in written form, with 3 exercises (for example: stereonet, testing of specimen, Mohr's circle, kinematic of blocks); then correction and 3 oral questions about theory of the program. Oral part is compulsory. Min score for admittance of written part 18/30, max score 30/30 (+L) for each part; final mark is the average of scores. Duration: written part about 90’, oral part 30'. Criteria to pass the exam: 1) solve in correct mode the exercises 2) demonstrate the ability to understand the interaction between natural and excavated structures and to solve basic analytic problems. 3) The exam is based on the didactical material loaded on the website and developed during the lessons. 4) The evaluation of the student skills is based on the ability to understand the key points to solve practical and common geo - engineering problems presented during the oral part of the exam..

Data science and machine learning for engineering applications

The exam includes a group project and a written part. The final score is defined by considering both the evaluation of the group project and the written part. The teacher may request an integrative test to confirm the obtained evaluation. Learning objectives assessment The written part will assess - the knowledge of the data preparation techniques and the major data mining algorithms for classification, regression, clustering, and association rule mining. - the knowledge of the machine learning and deep learning for engineering applications. The group project will assess - the ability to design, implement and evaluate a complete data science process, including the evaluation and tuning of machine learning algorithms and result assessment for a specific engineering application. - the working knowledge of the Python language and the major data mining and machine learning libraries. Exam structure and grading criteria The group project consists in designing and implementing a data science process, based on machine learning algorithms, for solving a data analytics task related to a specific engineering application. The project is assigned after 6 weeks of lectures. The evaluation of the group project is based on the performance and accuracy of the proposed solution, in terms of standard quality measures (e.g., prediction accuracy), and completeness (i.e., in depth analysis of each phase of the designed process and motivation for selecting given techniques and algorithms). The written part covers the theoretical part of the course. It includes multiple choice and box-to-fill questions related to the theoretical part of the course (data cleaning, data pre-processing, data mining algorithms, data science process, machine learning and deep learning algorithms). For multiple choice questions, wrong answers are penalized. The score of each question will be specified in the exam text. The written exam lasts 60 minutes. Textbooks, notes, electronic devices of any kind are not allowed. The maximum grade for the group project is 20. The maximum grade for the written part is 12. The final grade is given by the sum of the two parts. The exam is passed if the grade of the group project is greater than or equal to 10, the grade of the written part is greater than or equal to 8, and the overall grade is greater than or equal to 18. If the final score is strictly greater than 31 the registered score will be 30 with honor.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.