Alessandro Prada

Assistant Professor

Università degli Studi di Torino
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Italy

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Alessandro Prada is an Assistant Professor at Università degli Studi di Trento, Italy. His research focuses on energy efficiency and renewable energy systems, with recent publications exploring topics such as green hydrogen hubs, energy performance in renewable energy communities, and building energy simulations. Additionally, he investigates the impact of building materials and occupant interactions on energy management and sustainability. His work contributes to advancing knowledge in the fields of energy engineering and building performance.

Articles (15)

Timber-Based Strategies for Seismic Collapse Prevention and Energy Performance Improvement in Masonry Buildings

This study investigates the effectiveness of a range of timber-based solutions for the seismic and energy retrofitting of existing masonry buildings. These solutions are designed not only to prevent structural collapse during earthquakes but also to create integrated interventions that enhance thermo-physical performance and reduce emissions in existing buildings. Various case scenarios were considered and both mechanical and energetic behaviour post-intervention were evaluated. Timber-engineered products serve as foundational components for the retrofit approach, encompassing one-dimensional vertical elements (strong-backs) and various types of panels (cross-laminated timber panels, laminated veneer lumber panels, and oriented strand board panels). The analyzed retrofit techniques share a common principle involving the attachment of these timber-based elements to the building’s wall surfaces through mechanical point-to-point connections. The proposed solutions integrate strong-backs and timber panels with membranes and insulation layers, yielding cohesive, and highly effective interventions. Finite element modeling was employed to analyze the mechanical and thermal responses of the retrofitted walls. A comprehensive comparative analysis of various techniques was conducted to determine the most effective solution for each specific scenario.

Year:

2024

Collaborators (8)

Alessandro Pegoretti

Full professor

University of Trento

ITALY

Ivan Giongo

University of Trento

ITALY

vincenzo trovato

Imperial College London

UNITED KINGDOM

Vincenzo Corrado

Full Professor

Politecnico di Torino

ITALY

Cristina Cornaro

-

ITALY

Paolo Baggio

full professor

University of Trento

ITALY

Francesco Valentini

University of Trieste

ITALY

Adriana Angelotti

Assistant Professor

Politecnico di Milano

ITALY
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