Talking about the recovery process of Cultural Heritage, the systemic architectural entities represent a specific category. For them, the recurrent morpho-typological, functional and constructive features determine an additional level of relevance in the technical knowledge. Moreover, their geographical “extension” and technological level of “recurrences” influence and increase the complexity of their recovery process both during the analysis and the assessment phases. On the other hand, scientific experiences applying IoTs in Cultural Heritage highlighted the effectiveness of technologies in supporting the multiple levels of knowledge by technical users, as well as the practical expertise in managing them in all the recovery phases. With this aim, the paper introduces and discusses a smart methodological platform, Ar.KnoWeb, structured as a unique web-based page, where technical experts involved in the process can relate, manage and assess the class of systemic heritage, solving the technical and geographical levels of knowledge. Specifically, the platform allows the categorization of physical and qualitative information, as well as the identification of prevalent decays and recovery actions in a semi-automatic way, combining the potentialities of Digital Models, Machine and Deep Learning procedures and relational databases. The protocol has been finally tested in an interesting case of systemic Heritage: the historical Telegraphic Towers located along the paths between Madrid and Valencia. These, due to their previous studies by the authors already aimed at collecting historical, architectural, functional and constitutive features for their coherent retrofit.

Ar.KnoWeb (Architectural Knowledge Web). Una piattaforma metodologica per la conoscenza tecnica di architetture sistemiche / Lasorella, Margherita; Cantatore, Elena; de-dato, Pasquale; Fatiguso, Fabio. - ELETTRONICO. - (2022), pp. 1439-1454.

Ar.KnoWeb (Architectural Knowledge Web). Una piattaforma metodologica per la conoscenza tecnica di architetture sistemiche

margherita lasorella
;
Elena cantatore;Pasquale de-dato;fabio fatiguso
2022-01-01

Abstract

Talking about the recovery process of Cultural Heritage, the systemic architectural entities represent a specific category. For them, the recurrent morpho-typological, functional and constructive features determine an additional level of relevance in the technical knowledge. Moreover, their geographical “extension” and technological level of “recurrences” influence and increase the complexity of their recovery process both during the analysis and the assessment phases. On the other hand, scientific experiences applying IoTs in Cultural Heritage highlighted the effectiveness of technologies in supporting the multiple levels of knowledge by technical users, as well as the practical expertise in managing them in all the recovery phases. With this aim, the paper introduces and discusses a smart methodological platform, Ar.KnoWeb, structured as a unique web-based page, where technical experts involved in the process can relate, manage and assess the class of systemic heritage, solving the technical and geographical levels of knowledge. Specifically, the platform allows the categorization of physical and qualitative information, as well as the identification of prevalent decays and recovery actions in a semi-automatic way, combining the potentialities of Digital Models, Machine and Deep Learning procedures and relational databases. The protocol has been finally tested in an interesting case of systemic Heritage: the historical Telegraphic Towers located along the paths between Madrid and Valencia. These, due to their previous studies by the authors already aimed at collecting historical, architectural, functional and constitutive features for their coherent retrofit.
2022
Memoria e Innovazione Memory and Innovation
978-88-945937-4-7
Ar.KnoWeb (Architectural Knowledge Web). Una piattaforma metodologica per la conoscenza tecnica di architetture sistemiche / Lasorella, Margherita; Cantatore, Elena; de-dato, Pasquale; Fatiguso, Fabio. - ELETTRONICO. - (2022), pp. 1439-1454.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/262174
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