President
Prof. Walter Salvatore
e-mail:
This email address is being protected from spambots. You need JavaScript enabled to view it.
Administrative Office
Department Ingegneria Civile e Industriale
Overview and objectives of the PhD course
Existing constructions played a crucial role in the field of research and development of structural and geotechnical engineering, involving issues typical of modelling, experimental characterization, and monitoring.
In 2020, the Ministry of Infrastructure and Sustainable Mobility, MISM, provided new guidelines on the “Classification and Risk Management, Safety Assessment and Monitoring of Existing Bridges”, related to the civil infrastructural sector. The proposed procedures formalise a new approach to managing the bridge and viaduct infrastructural assets. Comparable guidelines have been approved for existing tunnels. The Guidelines’ approach is based on the evaluation of the so-called “level of attention”, discriminating factor in identifying the elements requiring in-depth evaluations, monitoring or retrofit/managing interventions.
The above-mentioned procedures are generally carried out by qualified experts using well-established surveying, investigation, modelling, and analysis techniques. These ‘conventional’ methods currently involve a needful process of information integration, data collection and processing that is demanding in terms of economic, human and time resources. As an answer to these requirements, technical and technological development is now receiving a breakthrough improvement. Increasingly precise and effective solutions are emerging, taking advantage of the most modern technologies in the fields of sensor technology, instrumental surveying, computerization, and artificial intelligence.
For instance, AI system is of particular interest today for real-time infrastructure damage detection and the assessment of its implications on the security level.
On the other hand, autonomous robot systems are becoming more popular both for data collection - by means of autonomous and/or semi-autonomous operations and for data integration from heterogeneous and/or mobile sources.
The aim of the PhD course is to train highly specialised experts able to combine advanced structural and geotechnical knowledge in the field of risk assessment and classification and in the modelling, assessment, control and monitoring of structures with the possibilities offered by data analysis and computerisation techniques, geometric and photogrammetric surveying and artificial intelligence.
The course envisages the setting up of a consortium among the University of Pisa, precisely the Department of Civil and Industrial Engineering as administrative headquarters, Scuola Superiore Sant'Anna, Class of Experimental and Applied Sciences, Autostrada dei Fiori S.p.A., a major motorway operator, and Engineering S.p.A., leading company in software field and IT services. The Consortium will also benefit from the scientific contributions of the Italian Institute of Technology in Genoa, with which it will establish an enduring collaboration.
The degree will be released jointly by University of Pisa and Scuola Superiore Sant'Anna.
The PhD programme involves rigorous basic courses provided by professors from University of Pisa, Scuola Superiore Sant'Anna and the Italian Institute of Technology, followed by in-depth specialist courses on specific topics, thanks to close cooperation between universities, research institutes, and industry.
Basic courses are planned to be organised in the areas of structural reliability, deep learning, computer vision and sensor technology. Along with them, specific courses will be provided in structural mechanics, new and existing building technology, robotics & sensors, as well as optional courses and seminars on topics of interest.
It is foreseen a compulsory stay abroad of the doctoral student of at least 9 months, in order to consolidate the acquired knowledge related to the thesis research field and get acquainted with new study, research, and experimental methods.
Doctoral students will have the possibility to interface continuously with counterparts from industry, consortia and non-consortia stakeholders, also by means of stays of variable length depending on needs.
Transfer of technology (TOT) will be assured not only through compulsory stays and internships but also thanks to the support and mentorship of company tutors, as well as by a marked technical, scientific and cultural interchange. The latter will be implemented in form of, for instance, seminars, events and thematic tables, together with the development of an open innovation centre for company access.
PhD tutors, of which at least one must come from the industrial field, will have to ensure consistency of the output with the research programme, promote relations with industry, supervise publications and pre-assess training activities and period abroad.
The course aims to provide a wealth of skills in order to cope with highly qualified research and very impact professional activities, on topics of modelling, monitoring, and control of civil structures.
The course deals with the research from a cross-disciplinary education point of view, comprising physical-mathematical modelling, computational mechanics, experimental mechanics, monitoring, current and innovative building techniques, geotechnics and structural diagnostics, as well as automation, robotics, sensor and control techniques, data processing, artificial intelligence (AI) and machine learning systems.
New technologies are progressively substituting traditional surveying, detecting, controlling, and monitoring tools commonly used for structures and infrastructures.
Innovative sensor systems come in all shapes forms, and functions, encompassing different types of cameras, morphological detectors, either fixed or mobile, autonomous vehicles, and robotic arms, capable of acquiring geometries and graphic information while also examining the surface and depicting structural defects. Various techniques have been proposed to merge geometric and graphic data collected by mobile vehicles able to get around the structure autonomously, semi-supervised, or by remote piloting.
The application of advanced classification and estimation techniques to field data has proven to be very useful for determining the type and scale of evolutionary phenomena as well as for calibrating and update of the mechanical and structural model, and for assessing structural safety levels. New machine learning and artificial vision methods are being developed, which take advantage of a set of images with annotated defects to quickly and semi-automated locate and classify evolutionary damage and degradation phenomena.
Further developments are of course possible by using cutting-edge surveying and monitoring methods based on mechanical and predictive models, which allow the real-time estimation of construction safety levels and thus its service life.
The course aims to provide the tools and the opportunity to develop research projects related to the following themes, which, albeit formally separate, share a joined-up approach:
- Structural and geotechnical modelling and design: the theme aims to merge together complementary features of study and research focusing on mechanical modelling of micro- and macro-structures, characterisation of the mechanical properties of traditional and innovative materials, dynamic and safety analysis of structures, experimental validation of adopted models, seismic-structural-hydrogeological and foundational risk assessment, development of predictive models of relevant evolutionary phenomena, monitoring and controlling of existing structures both in relation to anthropogenic load and natural phenomena, civil structures safety, data integration from analysis and monitoring, design of innovative solutions for the existing structure strengthening.
- Robotics, sensors, and embedded systems for data acquisition: the theme is based on the development of innovative infrastructure research and data collection methods for the definition of geometric models, the analysis of geomorphological data, and the correlation of infield data to project ones. Expertise will be developed in robotics and computer vision, the latter referencing both traditional sensors and new distributed, extended, or mobile sensors (such as radar, fiber optics, thermal sensors, and others)
- AI and Machine Learning: the content of this theme addresses information processing and risk forecast, acquisition and managing of visual, thermal, radar and acoustic images; computer vision techniques for image analysis (2D, 2.5D, 3D) research intended for classifying and quantifying defects; AI algorithms (machine learning and deep learning) development for automatic detection of anomalies and defects
PhD course’s cornerstone will be the ability to safeguard what has been achieved in the structural engineering field and at the same time make the most of robotic, machine learning and AI potentiality.
Regolamento interno del corso di dottorato (only in Italian)
Website
Under construction
