Five grants are available: 2 grants funded by Università degli Studi di Genova,  1 grant funded by DICCA on the Partenariato Esteso PE-RETURN (Spoke VS2), 1 grant funded by DICCA on the Partenariato Esteso PE-RETURN (Spoke VS3) and 1 grant funded on DM 118 (PA). 

For the two grants funded by DICCA on the Partenariato Esteso PE-RETURN (Spoke VS2 and Spoke SV3) and for the grant funded on DM 118 -PA, the research projects have been already fixed. For these grants, the research project of the candidate must agree with the goals identified for this research theme in the following.

For the other two grants, funded by Università degli Studi di Genova, possible research topics are described in the following. Interested Candidates are invited to contact the proposers listed below or other members of the Internal Curriculum Committee for agreeing on other possible topics (the complete list of members is available here).

Possible research topics for the two grants funded by the University of Genova

Research topic 1

Title: Modelling wind and temperature in urban environments for strengthening resilience and adaptation capacity to climate change

Proposer: Massimiliano Burlando

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description:  10 -15  lines

Climate Change (CC) and continuous urbanization stress the resilience of our urban societies and the built environment that supports them. CC has caused an increasing frequency of extreme climatic events: Meteorological events (e.g., category 5 storms), as well as climatological events (e.g., heat waves), have more than doubled since 1980 [Cigre, 2021]. Extreme temperatures rank first in terms of fatalities regarding natural hazards and technological accidents in Europe, and windstorms rank second in terms of overall economic losses in the EU [Spinoni et al. 2020]. Accordingly, there is a pressing need to increase urban resilience to windstorms and extreme temperatures.

Climate model projections of extreme winds and temperatures are highly uncertain at the urban scale because the current generation of climate models do not resolve the required spatial and temporal scales. This can be done using advanced Computational Fluid Dynamics (CFD) techniques, coupled with additional modules for conduction, convection and radiation. The present research focuses on the realization of a CFD model of a real urban environment to study windstorm effects on strategic assets as well as Urban Heat Island (UHI) and heat waves. The model is then used to test different mitigation/adaptation strategies to CC targeting at increasing the resilience of modern cities.

For more information please contact: Prof. Massimiliano Burlando, massimiliano.burlando@unige.it

Link to the group or personal webpage: https://www.gs-windyn.it/

References: 

o   Barlow J.F. (2014). Progress in observing and modelling the urban boundary layer. Urban Climate 10, 216-240

o   Toparlar Y., B. Blocken, P. Vos, G.J.F. van Heijst, W.D. Janssen, T. van Hooff, H. Montazeri, H.J.P. Timmermans (2015). CFD simulation and validation of urban microclimate: A case study for Bergpolder Zuid, Rotterdam. Building & Environment 83, 79-90

o   Sihong Du, Xinkai Zhang, Xing Jin, Xin Zhou, Xing Shi (2022). A review of multi-scale modelling, assessment, and improvement methods of the urban thermal and wind environment. Building and Environment 213, 108860

o   Ricci A., M. Burlando, M.P. Repetto, B. Blocken (2022). Static downscaling of mesoscale wind conditions to an urban canopy layer by a CFD microscale model. Building and Environment 225, 109626

Research topic 3

Title: Developing Decision Support Systems to plan, design and install nature-based solutions in urban areas for the mitigation of the pluvial flooding. 

Proposers: Anna Palla, Ilaria Gnecco

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description:  Pluvial flooding has become one of the most frequent natural disasters in recent years, and the pairing of nature-based solutions with the traditional grey infrastructure is recognized as the solution to mitigate the negative impact of urbanisation on the hydrological response.

The main objective of the present research was to develop a Decision Support Systems (DSS) for planning the interventions (nature-based solutions and traditional grey infrastructure) at the catchment scale to enhance urban resilience to cope with intense rain events with an insight into both current and future climate. It will be based on the implementation of a multi-objective optimization algorithm. Compared to other existing methodologies in the scientific literature, the methodology will be structured according to the phasing-of-construction/rehabilitation approach, aiming to design interventions in phases rather than all at once at the beginning of the construction, with emphasis to budget limits and availability in time.

The research associated with the multi-phase optimization DSS is essential to enable practitioners and policy-makers to design short-term upgrades of the urban drainage network, aimed at reaching pre-fixed levels of reliability while fitting the expected growth and development of the system in the long term.

For more information please contact: Anna Palla, anna.palla@unige.it , Ilaria Gnecco, ilaria.gnecco@unige.it

References: 

o   Mei, C., Liu, J., Wang, H., (...), Ding, X., Shao, W. Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed. Sci. Total Environ., 2018, 639, 1394-1407.

o   Palla, A., Gnecco. I. On the Effectiveness of Domestic Rainwater Harvesting Systems to Support Urban Flood Resilience. Water Resource Management, 2022, 36(15), 5897–5914.

o   Palla, A., Gnecco, I. The web-gis TRIG eau platform to assess urban flood mitigation by domestic rainwater harvesting systems in two residential settlements in Italy. Sustainability, 2021, 13(13), 7241.

Research topic 4

Title: Machine-learning modeling to design micro-structured concrete absorber of carbon dioxide (CO2)

Proposer:     Antonio Caggiano

This project aims to create an opportunity for a collaboration between the University of Genova in Italy and the Leibniz Universität Hannover in Germany thanks to the availability of Prof.  Fadi Aldakheel as co-supervisor. There is also a possible mobility period of up to 18 months at the German institution.

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description: This proposal has the aim of developing a machine learning-based modelling tool that can be employed to shape the microstructure of concrete to enhance its properties, including its ability to absorb carbon dioxide (CO2). How machine learning can be applied:

·      Step-1: data collection to train a machine-learning model dealing with dataset consisting of concrete microstructural featuring CO2 absorption measurements. The features could include parameters like cement composition, aggregate properties, water-cement ratio, curing conditions, and any other relevant factors.

·      Step-2: feature engineering data to select and transform input microstructural variables (features) into a suitable format for the machine-learning model. Domain knowledge of concrete chemistry and microstructures are key steps.

·      Step-3: model training upon the prepared dataset. Selection of ML algorithms need to be selected based on the complexity of the problem and the size of the dataset.

·      Step-4: model validation and optimization by using appropriate and once the model is trained and validated, prediction of CO2 absorption capacity of new concrete mixtures based on their microstructural features can be made. 

By using machine learning in this way, researchers and engineers can efficiently explore the vast design space of concrete microstructures and identify compositions that maximize CO2 absorption capabilities while meeting other performance requirements. This can contribute to the development of more sustainable and environmentally friendly construction materials.

For more information, please contact:  Prof. Antonio Caggiano, antonio.caggiano@unige.it https://www.scopus.com/authid/detail.uri?authorId=54916174800

References: 

o   Caggiano, A., Peralta, I., Fachinotti, V. D., Goracci, G., & Dolado, J. S. (2023). Atomistic simulations, meso-scale analyses and experimental validation of thermal properties in ordinary Portland cement and geopolymer pastes. Comp. & Structures, 285, 107068. doi.org/10.1016/j.compstruc.2023.107068

o   Aldakheel, F., Elsayed, E. S., Zohdi, T. I., & Wriggers, P. (2023). Efficient multiscale modeling of heterogeneous materials using deep neural networks. Computational Mechanics 72, 155–171. https://doi.org/10.1007/s00466-023-02324-9

o   Yang, S., Aldakheel, F., Caggiano, A., Wriggers, P., & Koenders, E. (2020). A review on cementitious self-healing and the potential of phase-field methods for modeling crack-closing and fracture recovery. Materials, 13(22), 5265: https://doi.org/10.3390/ma13225265

Research topics proposed for the two grants funded by DICCA on the Partenariato Esteso PE-RETURN (Spoke VS2 and Spoke SV3) and for the grant on DM 118 -PA. For these grants, the research project of the candidate must agree with the goals identified for the research theme in the following.

Research topic “PE-RETURN - Spoke VS3”

Grant funded by DICCA on the Partenariato Esteso PE-RETURN (PNRR - Missione 4 Istruzione e ricerca - Componente 2 Dalla ricerca all’impresa – Investimento 1.3 – PE RETURN “Multi-Risk Science for Resilient Communities Under a Changing Climate”) - Spoke VS3

Title: Development of decision support tools based on SHM data and low-cost sensors for seismic damage scenario at urban scale and for assessing the structural usability and safety of strategic buildings

Proposers: Serena Cattari, Simone Barani

Curriculum: Risk and Resilience Engineering for the Natural, Industrialized and Built Environments

Description:  

The research will leverage the recent technological advancements in seismic monitoring and data processing for a twofold objective, to improve the evaluation of damage scenarios at the urban scale and to assess the post-earthquake usability of strategic buildings involved in seismic emergency plans. In this context, a leading role is being played by low-cost Micro Electro-Mechanical Systems (MEMS) (D’Alessandro et al., 2019), nowadays widely used to densify permanent networks of accelerometers, improve seismic detection, and evaluate the effects of earthquakes with greater resolution (e.g., Vitale et al., 2022). Concurrently, the increasing amount of data has fueled the development of efficient automatic procedures (e.g., Scafidi et al., 2019), so that the related results (i.e., earthquake location, magnitude, and shaking maps) can be shared in real time with the end users. The final shaking maps combine real ground-motion data from station recordings with data predicted by a ground-motion prediction equation (GMPE). Predicted data, however, are affected by large epistemic uncertainty, which reflects the uncertainty in site conditions (i.e., soil type based VS,30) and in the choice of the GMPE. Reducing epistemic uncertainty implies the densification of seismic networks through the installation of new stations, so as to cover the monitored area extensively. The extensive use of low-cost MEMS goes in that direction, as it allows increasing the reliability of shaking maps.

Similarly, the installation of MEMS accelerometers on strategic buildings (Dolce et al., 2017) is extremely valuable to monitor their structural performance during earthquakes as well as to calibrate computational models (Sivori et al., 2021), providing detailed information (i.e. the variation on the frequencies and modal shapes as well as the estimate of interstory drift values) useful for assigning the level of occurred damage which could limit the building functionality and structural safety. Moreover, sensor network can also improve the reliability of damage scenarios by identifying representative parameters of typological structural classes (Astorga et al., 2020, Gallipoli et al, 2023). 

The research project aims to explore the potential of the seismic monitoring at the above-mentioned scales in order to address the development of decision support tools, particularly useful for the management of emergency phase after disastrous event.

For more information please contact: Serena Cattari (serena.cattari@unige.it), Simone Barani (simone.barani@unige.it) 

Link to the group or personal webpage: 

https://rubrica.unige.it/personale/UkNHUl5s

https://rubrica.unige.it/personale/UkNGW19h

References: 

o   D’Alessandro, A., Scudero, S., & Vitale, G. (2019). A review of the capacitive MEMS for seismology. Sensors, 19(14), 3093. doi:10.3390/s19143093, 2019.

o   Vitale, G., D’Alessandro, A., Di Benedetto, A., Figlioli, A., Costanzo, A., Speciale, S., Quintilio, P., & Cipriani, L. (2022). Urban Seismic Network Based on MEMS Sensors: The Experience of the Seismic Observatory in Camerino (Marche, Italy). Sensors, 22(12), 4335. doi:10.3390/s22124335, 2022.

o   Scafidi, D., Spallarossa, D., Ferretti, G., Barani, S., Castello, B., & Margheriti, L. (2019). A complete automatic procedure to compile reliable seismic catalogs and travel‐time and strong‐motion parameters datasets. Seismological Research Letters, 90(3), 1308-1317. doi:10.1785/0220180257

o   Dolce, M., Nicoletti, M., De Sortis, A., Marchesini, S., Spina, D., & Talanas, F. (2017). Osservatorio sismico delle strutture: the Italian structural seismic monitoring network. Bulletin of Earthquake Engineering, 15, 621-641. doi:10.1007/s10518-015-9738-x

o   Sivori, D., Cattari, S., & Lepidi, M. (2022). A methodological framework to relate the earthquake-induced frequency reduction to structural damage in masonry buildings. Bulletin of Earthquake Engineering, 20(9), 4603-4638. doi:10.1007/s10518-022-01345-8

o   Astorga, A., Guéguen, P., Ghimire, S., & Kashima, T. (2020). NDE1. 0: a new database of earthquake data recordings from buildings for engineering applications. Bulletin of Earthquake Engineering, 18, 1321-1344. doi:10.1007/s10518-019-00746-6

o   Gallipoli, M. R., Petrovic, B., Calamita, G., Tragni, N., Scaini, C., Barnaba, C., Vona, M., & Parolai, S. (2023). Towards specific T–H relationships: FRIBAS database for better characterization of RC and URM buildings. Bulletin of Earthquake Engineering, 1-27. doi: 10.1007/s10518-022-01594-7