Research topic B

Title: Seismic assessment of historic reinforced concrete frames with thick masonry infills 

Proposers: Chiara Calderini, Stefano Podestà

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

Description:  Reinforced concrete (RC) frames constructed in the early 20th century often feature thick stone or brick masonry infills. However, the interaction between these RC frames and such thick masonry infills poses significant challenges in structural modeling, particularly under seismic loading conditions. This research aims to develop a comprehensive structural model that accurately represents the complex interaction between early 20th century RC frames and thick masonry infills.

The study will focus on the following objectives: (1) investigating the mechanical behavior of thick masonry infills and their impact on the overall structural performance of RC frames, (2) developing analytical models that incorporate the nonlinear behavior of both the RC frame and the infill, and (3) validating these models through experimental testing and finite element analysis (FEA).

Methodologically, the research will encompass a thorough literature review, material characterization tests, analytical modeling, FEA simulations and, if possible, experimental validation. Expected outcomes include a validated analytical model capable of predicting the behavior of RC frames with thick masonry infills under various loading scenarios and improved guidelines for assessing and retrofitting historical RC structures.

This research will significantly contribute to structural engineering by enhancing the understanding of RC frame and masonry infill interactions, thus aiding in the preservation and safety improvement of historically significant buildings. 

For more information please contact: Chiara Calderini, chiara.calderini@unige.it

Link to the group or personal webpage: https://rubrica.unige.it/personale/UkNHX1Nr

References: 

Morandi, P., Hak, S., Milanesi, R., Magenes, G. (2022). In-plane/out-of-plane interaction of strong masonry infills: From cyclic tests to out-of-plane verifications, Earthquake Engineering and Structural Dynamics, Volume 51 (3), 648 – 672.

Rostamkalaee, S., Peloso, S., Brunesi, E. (2023). Macro-Modelling of IP-OoP Interaction in Unreinforced Solid Masonry Infills under Earthquake-Induced Actions: A Review. Buildings, 13(9),2326.

Baek, E.R., Pohoryles, D.A., Bournas, D. (2024). Seismic assessment of the in-plane/out-of-plane interaction of masonry infills in a two-storey RC building subjected to bi-directional shaking table tests, Earthquake Engineering and Structural Dynamics, 53(6), pp. 2230-2251

Research topic E

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 urbanization 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

Link to the group or personal webpage: -

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 BF1

Grant cofunded by MUR/ Department DICCA

Title: Enhancing the efficiency of structural monitoring in assessing the seismic damage of strategic buildings

Proposers: Serena Cattari

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

Description:  There is a growing search for cutting-edge technologies to enable effective and large-scale management of structures in urban contexts. Structural monitoring has gained great importance in this field, by allowing real-time assessment of the health conditions of structures and infrastructures based on robust experimental vibration data, giving rise to the established field of Structural Health Monitoring (SHM). The potential of such data is emerging also in the context of Seismic Structural Health Monitoring (S2HM) (Limongelli et al. 2019). In this context, the Italian Seismic Observatory of Structures (OSS), established in the mid-1990s, stands out as a significant achievement in the field of seismic and structural monitoring. The network monitors today more than one hundred and seventy strategic structures across the national territory. 

This research project aims to advance S2HM techniques for existing buildings by exploiting also the data available from this network.  Available will be used not only to deepen the response of single structures but also to outline a prototypal database to interpret phenomena (such as frequency co-shift, floor amplification seismic actions, …) and corroborate evidences on a large set of buildings.

The research methodology intends adopting multiple tools to interpret the seismic response of monitored structures such as: data driven approaches; machine-learning algorithms; and physic-based surrogate models that make use of numerical models. The final goals is to develop effective tools for assessing the damage and supporting also usability assessment crucial in the aftermath of seismic events.

For more information please contact: Serena Cattari, serena.cattari@unige.it

Additional information: the research presupposes a strict interaction with the Italian Seismic Observatory of Structures and also the collaboration with the LUNITEK company. 

Link to the group or personal webpage: 

Serena Cattari personal webpage: https://rubrica.unige.it/personale/UkNHUl5s 

References: 

o   Spina D, Lamonaca B, Nicoletti M, et al (2011) Structural monitoring by the Italian Department of Civil Protection and the case of 2009 Abruzzo seismic sequence. Bulletin of Earthquake Engineering 9(1):325 – 346. https://doi.org/10.1007/s10518-010-9232-4

o   Dolce M, Nicoletti M, De Sortis A, et al (2017) Osservatorio sismico delle strutture: the Italian structural seismic monitoring network. Bulletin of Earthquake Engineering 15(2):621 – 641. https://doi.org/10.1007/s10518-015-9738-x

o   Limongelli MP, Dolce M, Spina D, et al (2019) S2HM in some European countries. Springer Tracts in Civil Engineering p 303 – 343. https://doi.org/10.1007/978-3-030-13976-6 13

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. https://doi.org/10.1007/s10518-022-01345-8

o   Garcıa-Macıas E, Ierimonti L, Venanzi I, et al (2021) An Innovative Methodology for Online Surrogate-Based Model Updating of Historic Buildings Using Monitoring Data. International Journal of Architectural Heritage 15(1):92 – 112. https://doi.org/10.1080/15583058.2019.1668495

o   Garcıa-Macıas E, Hernandez-Gonzalez I, Puertas E, et al (2024) Meta-Model Assisted Continuous Vibration-Based Damage Identification of a Historical Rammed Earth Tower in the Alhambra Complex. International Journal of Architectural Heritage 18(3):427 – 453. https://doi.org/10.1080/15583058.2022. 2155883

o   Astorga AL, Gueguen P, Rivi`ere J, et al (2019) Recovery of the resonance frequency of buildings following strong seismic deformation as a proxy for structural health. Structural Health Monitoring 18(5-6):1966 – 1981. https://doi.org/10.1177/1475921718820770

o  Astorga, A., Guéguen, P., Ghimire, S. et al. NDE1.0: a new database of earthquake data recordings from buildings for engineering applications. Bull Earthquake Eng18, 1321–1344 (2020). https://doi.org/10.1007/s10518-019-00746-6

Research topic BF2

grant funded within D.M. 630 dated 24.4.2024 (co-funded by ETT S.p.a)

Title: Automatic crack detection from images, participatory digital tools and machine learning algorithms to support the seismic damage interpretation of existing buildings

Proposers: Bianca Federici, Serena Cattari, Antonio Novellino and Luca Oneto

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

Description:  

In recent years, several research projects have been launched aimed at automatically processing images for the interpretation of structural damage: so far, the applications are mostly found in tools that can support structural monitoring activities on individual artifacts.

The proposed project aims to develop a methodology to support the interpretation of damage of existing buildings in the aftermath of seismic events exploiting the potential of new technologies and tools, such as automatic crack detection from images, participatory digital tools and machine learning algorithms. The research intends supporting the attribution of synthetic damage levels at scale of single assets with the final goal to improve also the assessment of seismic impact at urban scale.

The attention will be focused either on monumental assets, such a churches, and residential buildings.

Advanced survey techniques will be used, including photogrammetry from drone and laser scanner with SLAM (Simultaneous Localization and Mapping) technology. A crack detection model has been recently developed by the UniGE research group using a deep learning model, the Convolution Neural Network, applied to images relative to churches; the same or other models could be applied to other typologies of existing buildings.

Images will be automatically interpreted to identify cracks and their geometric characteristics, i.e. length and width.  Then, appropriate tools will be developed to determine the cracks position in the 3D space or on a plan or façade orthophoto, for supporting the interpretation at the scale of single assets. The potential of machine learning algorithms will be adopted to elaborate large dataset of data (either available from real seismic events or numerically generated by other research experiences) and to address the design of dedicated dashboard useful to support engineers in the attribution of synthetic damage levels (Cattari and Angiolilli 2022).

At urban scale, the images, and therefore the identified damages, will then be georeferenced to be automatically managed in a Geographic Information System (GIS) for mapping the consequences of the seismic shaking. Participatory digital tools (like MUST : https://ettsolutions.com/progetti/must/) will be explored and further developed to speed up and integrate the acquisition of damage information with the aim of supporting the rapid collection of data that are also useful to update and validate damage scenario estimated from mechanical/empirical approaches.

For more information please contact:  Bianca Federici, bianca.federici@unige.it Serena Cattari, serena.cattari@unige.it

Link to the group or personal webpage: 

Bianca Federici personal webpage: https://rubrica.unige.it/personale/UkNHWV5q 

Serena Cattari personal webpage: https://rubrica.unige.it/personale/UkNHUl5s 

References: 

o   Cattari, S., Angiolilli, M. Multiscale procedure to assign structural damage levels in masonry buildings from observed or numerically simulated seismic performance. Bull Earthquake Eng 20, 7561–7607 (2022). https://doi.org/10.1007/s10518-022-01504-x

o   Golding, V.P.; Gharineiat, Z.; Munawar, H.S.; Ullah, F. Crack Detection in Concrete Structures Using Deep Learning. Sustainability 2022, 14, 8117. https://doi.org/10.3390/su14138117.

o   Munawar, H. S., Ullah, F., Heravi, A., Thaheem, M. J., & Maqsoom, A. (2022). Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages. Drones, 6(1), 5. https://doi.org/10.3390/drones6010005

o   Rezaie, A.; Achanta, R.; Godio, M.; Beyer, K. Comparison of crack segmentation using digital image correlation measurements and deep learning. *Construction and Building Materials*, 261, 120474 (2020). https://doi.org/10.1016/j.conbuildmat.2020.120474

o   Giacco, G., Mariniello, G., Marrone, S., Asprone, D., Sansone, C. (2022). Toward a System for Post-Earthquake Safety Evaluation of Masonry Buildings. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_26