2023–2024 · Taranto, Italy
Parametric and computational design study for the Grottaglie spaceport hangar, combining genetic algorithm-based optimization with a constructability strategy based on the standardization of building components.
Role: Parametric Computational Design & Structural Optimization
Partners
Rome, Italy
Preliminary and parametric structural design of a pedestrian walkway linking Terminal 3 to the Satellite Building, designed to maintain operational clearance for aircraft beneath the structure.
Role: Preliminary & Parametric Structural Design.
Partners
2024 · Dakar, Senegal
Form finding and nonlinear stability assessment of the Dakar Mosque gridshell roof, characterized by a form-found geometry inspired by desert dunes.
Role: Form Finding & Non-linear Post-buckling Analysis
Partners
2022 · Salerno, Italy
Preliminary computational design, form finding, and structural optimization study for a ~10,000 m² gridshell roof concept developed during the design exploration phase for the Maximall Pompei shopping center.
Role: Computational design, Form Finding & Structural Optimization
Partners
A free Grasshopper plug-in implementing the improved Multi-body Rope Approach (i-MRA) for gridshell form finding in Rhinoceros 3D, providing parametric support for practitioners and designers.
Built for Rhinoceros 3D and Grasshopper.
Collaborators:A. Manuello Bertetto · J. Melchiorre · G.C. Marano · G. Rosi · S. Sotiropoulos · A. Brentegani
Get it on Food4Rhino:
2022 & 2024 · MSCA-RISE · Horizon 2020
Additively Manufactured Optimized Structures by means of Machine Learning (GA 101007595). Staff-exchange research on ML-based structural optimization, hosted at NTUA and Infersence in Athens (Prof. N. D. Lagaros).
Role: Visiting researcher
Partners
2023–2025 · PNRR · NRRP
Participation in Spoke 7 “Edge-Exascale AI” of the FAIR extended partnership at Politecnico di Torino.
Role: Researcher - Spoke 7, Edge-Exascale AI
Partners
2022–2025 · PNRR · MUR (ECS00000036)
Participation in the Space4You project within the NODES PNRR ecosystem, contributing to the small-satellite flagship initiative of Spoke 1 on Aerospace and Sustainable Mobility.
Role: Researcher - Space4You (Spoke 1, Aerospace)
Partners
2023–2026 · Politecnico di Torino
An applied machine learning project for concrete mix design, predicting strength- and workability-related properties from mixture composition and production-site environmental data. By integrating mix proportions with weather-related information from batching plants, the approach supports more accurate performance prediction and can help reduce unnecessary cement overuse while maintaining target mechanical requirements.