TEMA Journal is committed to publishing articles in the issue immediately after their acceptance through the double-blind review phase, in the form of a Just Accepted Article. This method, already practiced by numerous scientific journals, provides an additional and free service for Authors as well as the entire scientific community, as it significantly accelerates the dissemination of contributions with a fully citable DOI (Digital Object Identifier) compared to the longer publication times of complete issues.
To this end, following the Authors’ explicit request, the Editorial Board populates the Just Accepted page with the accepted articles that have successfully passed the review process.
Just Accepted articles are published, as received by the Author or in a pre-print version, in the form of PDF documents.
Each Just Accepted Article features a header, names of the authors, an abstract, and a Digital Object Identifier (DOI), with the watermark “Just Accepted” visible on each page. The DOI will also remain unchanged for the article published in the final issue.
Since they are not included in the final version, Just Accepted articles are removed from the TEMA Journal website at the time of the online publication of the definitive article.
After being published as Just Accepted, the article follows the standard production process (editorial revision, proofreading of the English language, layout, revision of the layout draft, online publication, and revision before printing of the issue).
ARTICLES JUST ACCEPTED
A Philological Digital Platform to Experimental Preservation: Upcycling The Prefabricated School Buildings Heritage
Ilaria Giannetti1*, Fabiano Micocci2, and Angelo Bertolazzi3
1* – Department of Civil Engineering and Computer Science Engineering, University of Rome Tor Vergata
2 – Department of Architecture, University of Thessaly
3 – Department of Civil Engineering and Computer Science Engineering, University of Padua
Abstract
In Europe, the increasing demand for public education between the 1950s and 1960s prompted extensive school-building programs. The design of these new schools was supported by updated pedagogical theories, which inspired a rethinking of building layouts. From a technological point of view, intensive experimentation with prefabricated construction systems was carried out to meet emerging design concepts for school buildings, as well as to accelerate construction and reduce costs. Nowadays, late 20th-century school buildings have emerged as fragile architectural heritage, characterized by experimental technological solutions that require the development of customized preservation approaches. This contribution presents a philological digital platform to document and analyze exemplary late 20th-century school buildings: this platform aims to support the conception of a novel preservation strategy driven by the principles of the circular economy. The analysis is framed within the broader scenario of participatory practices for the experimental preservation of late 20th-century public building heritage.
Primary Contact: Ilaria Giannetti, ilaria.giannetti@uniroma2.it
Resilience and Operability in Post-Disaster 1 Scenarios: Case Study of a Defined Set of 2 Churches after the L’Aquila Earthquake
Cristina Cantagallo1*, and Valentino Sangiorgio1
1* – Department of Engineering and Geology (INGEO), University “G. d’Annunzio” of Chieti-Pescara, Italy
Abstract
The digitization of built heritage is essential for safeguarding cultural and historical assets, particularly in the face of disruptive events. In this context, this paper assesses the resilience and operability of existing churches, supported by a comprehensive digitization workflow and a large dataset of data. Specifically, the work focuses on 26 churches of the Sulmona-Valva Diocese damaged during the 2009 L’Aquila earthquake. The proposed workflow integrates systematic data collection, the development of empirical and theoretical resilience curves, and the calculation of a Global Resilience Index. Unlike traditional methodologies, this study incorporates restoration funds as a weighting factor in resilience assessments, reflecting the cultural and historical importance of each structure. Additionally, the integration of data into a flexible digital platform enables real-time analysis and resilience planning, supporting informed decision-making for urban planning and resource allocation. These digital platforms significantly enhance the resilience assessment of cultural heritage by enabling the storage and processing of large datasets, thereby revolutionizing both academic research and operational practices. The findings highlight the potential of a data-driven framework to enhance the protection and conservation of heritage buildings in seismic-prone areas.
Primary Contact: Cristina Cantagallo, cristina.cantagallo@unich.it
Mining Users’ Perceptions through Sentiment and Emotion Analysis to Address Heritage Conservation Strategies
Marco D’Orazio1, Elisa di Giuseppe1, and Maria Francesca Muccioli1*
1* – DICEA Dept., Università Politecnica delle Marche, Ancona, Italy
Abstract
Monitoring architectural heritage is a crucial step in the planning of proper conservation strategies and resource allocation. Current protocols rely on periodic, though infrequent, expert-led inspections, which assess the state of conservation of heritage assets and inform intervention priorities. However, public perceptions, which may suggest alternative courses of action, are seldom considered. This study proposes an innovative methodology integrating public feedback into heritage monitoring via Natural Language Processing (NLP). The framework, applied to ‘70s heritage sites in Italy’s Marche region, integrates Aspect-Based Sentiment Analysis (ABSA) and Aspect-Based Emotion Analysis (ABEA) to systematically analyze user-generated content, identifying heritage-related aspects and classifying sentiment (positive, negative, neutral) and emotions (e.g., joy, anger) from Google Maps reviews. Heritage-specific targets were first identified in user reviews using spaCy-based tokenization. Sentiment classification (positive, negative, neutral) was performed using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, while emotions (joy, anger, sadness, fear) were identified using the FEEL-IT algorithm. User perceptions were effectively retrieved, revealing a generally positive sentiment and joy as the most dominant emotion. This approach enables large-scale monitoring based on continuously updated user feedback, which can be integrated into current monitoring protocols to adopt a more comprehensive decision-making approach.
Primary Contact: Maria Francesca Muccioli, m.muccioli@pm.univpm.it
