Metodologie integrate HBIM per la conservazione e valorizzazione del patrimonio storico. Il caso della chiesa di Santa Maria del Carmine di Pisa.

Lo studio della chiesa di Santa Maria del Carmine di Pisa si articola su due macro argomenti fondamentali in cui lo sviluppo tecnologico rappresenta l’anello congiunzionale tra di essi. La ricerca storica risulta un supporto essenziale alla creazione di un modello tridimensionale atto a mostrare la ricostruzione ipotetica della configurazione medievale che si inserisce in un ampio progetto di indagine sulle chiese degli ordine mendicanti e i loro tramezzi condotto in collaborazione la University of Cambridge, l’Università di Suor Orsola Benincasa di Napoli e L’università di Padova. È stato compiuto un lavoro che ha toccato molteplici aspetti eterogenei relativi all’architettura e all’ingegneria confluendo nella realizzazione di un Building Information Model rappresentante un edificio storico. L'integrazione di diverse tecniche e la sperimentazione di nuovi approcci hanno permesso l’ottenimento di un modello tridimensionale fedele all’aspetto odierno della chiesa, corredato da uno specifico database che ha raccolto in un unico oggetto diverse tipologie di informazioni comprovando l’estrema utilità propria dell’odierno processo informato. Le problematiche riscontrate durante il percorso si sono trasformate in stimoli per compiere passi volti alla risoluzione di queste attraverso la sperimentazione di nuove tecniche basate sull’utilizzo dell’intelligenza artificiale che hanno permesso di colmare alcuni divari presentatisi in alcune fasi specifiche dell’intero processo. The study of the church of Santa Maria del Carmine in Pisa is articulated on two fundamental macro topics in which the technological development represents the junctional ring between them. The historical research is an essential support to the creation of a three-dimensional model able to show the hypothetical reconstruction of the medieval configuration that is part of a wide project of investigation on the churches of the mendicant orders and their partitions conducted in collaboration with the University of Cambridge, the University of Suor Orsola Benincasa in Naples and the University of Padua. The work has touched many heterogeneous aspects related to architecture and engineering and has led to the realization of a Building Information Model representing a historical building. The integration of different techniques and the experimentation of new approaches have allowed the achievement of a three-dimensional model faithful to the current appearance of the church, accompanied by a specific database that has collected in a single object different types of information proving the extreme usefulness of today's informed process. The problems encountered during the path have become stimuli to take steps to resolve them through the testing of new techniques based on the use of artificial intelligence that have allowed to fill some gaps presented in some specific phases of the entire process

Semantic mapping of decay and materials through Artificial Intelligence and integrated H-BIM management

International audienceNowadays, the digital documentation of architectural heritage necessarily requires the integration of different types of representation and the organization of information on different levels, in order to plan adequate restoration and conservation operations [1]. Semantic segmentation techniques relying on Artificial Intelligence are emerging in this field as privileged tools to appropriately organize, structure and classify the complex system of analytical and survey data related to an architectural object or site [2]–[4].In this contribution, semi-automatic classification methods are exploited in order to associate the different semantic and descriptive information to the raw outputs of the three-dimensional survey and H-BIM based representations are finally created to display the results. The case study on which the methodological approach is tested is a church located in Pisa (Italy), Chiesa del Carmine: the classification is performed on the liturgical decorative apparatus of the church, and the textured meshes of the altars are analyzed in order to characterize the state of preservation, and in particular the material and decay mapping of these objects.Taking as input data the raw 3D models derived from laser scanning or photogrammetry, a supervised Machine Learning (ML) algorithm is applied in order to read, classify and return different degrees of degradation and/or types of materials of the altars. In detail, starting from the 3D survey, orthophotos and UV maps are generated. On these, the classes of materials or the levels of the degradation are identified and annotated over a reduced portion, and this constitutes a set of samples on which the ML model is trained. The training data are also supported by the so-called features, i.e. radiometric or geometric characteristics that allow to distinguish one class from another [5].A predictive model (Random Forest) is trained on these data so as to foresee and map the classification of the entire dataset. Once this supervised classification is performed on the orthophoto or UV map, the results are projected onto the 3D by exploiting the projective relationships between images and model, and this allows to obtain an overall mesh model in which different colors correspond to different degrees of degradation or different types of material.This distinction into classes is preserved even at a final stage, when the classified mesh models derived from the segmentation process are inserted within specific H-BIM platforms, in a Scan-to-BIM application. To this end, portions of mesh having different material and degradation characteristics are imported into BIM platforms thanks to visual programming algorithms implemented in Rhino’s Grasshopper. This step ensures the autonomous management and informatization of each class derived from segmentation, and the semantic data can be more easily shared, retrieved, visualized and stored, also in view of the use of heritage models for augmented reality applications.The results obtained in terms of description and semantic mapping of the model and of traceability and retrieval of information in H-BIM environment suggest the extension of the proposed methodological approach to the study of ornamental apparatuses related to other churches of the Carmelite order

Semantic mapping of decay and materials through Artificial Intelligence and integrated H-BIM management

International audienceNowadays, the digital documentation of architectural heritage necessarily requires the integration of different types of representation and the organization of information on different levels, in order to plan adequate restoration and conservation operations [1]. Semantic segmentation techniques relying on Artificial Intelligence are emerging in this field as privileged tools to appropriately organize, structure and classify the complex system of analytical and survey data related to an architectural object or site [2]–[4].In this contribution, semi-automatic classification methods are exploited in order to associate the different semantic and descriptive information to the raw outputs of the three-dimensional survey and H-BIM based representations are finally created to display the results. The case study on which the methodological approach is tested is a church located in Pisa (Italy), Chiesa del Carmine: the classification is performed on the liturgical decorative apparatus of the church, and the textured meshes of the altars are analyzed in order to characterize the state of preservation, and in particular the material and decay mapping of these objects.Taking as input data the raw 3D models derived from laser scanning or photogrammetry, a supervised Machine Learning (ML) algorithm is applied in order to read, classify and return different degrees of degradation and/or types of materials of the altars. In detail, starting from the 3D survey, orthophotos and UV maps are generated. On these, the classes of materials or the levels of the degradation are identified and annotated over a reduced portion, and this constitutes a set of samples on which the ML model is trained. The training data are also supported by the so-called features, i.e. radiometric or geometric characteristics that allow to distinguish one class from another [5].A predictive model (Random Forest) is trained on these data so as to foresee and map the classification of the entire dataset. Once this supervised classification is performed on the orthophoto or UV map, the results are projected onto the 3D by exploiting the projective relationships between images and model, and this allows to obtain an overall mesh model in which different colors correspond to different degrees of degradation or different types of material.This distinction into classes is preserved even at a final stage, when the classified mesh models derived from the segmentation process are inserted within specific H-BIM platforms, in a Scan-to-BIM application. To this end, portions of mesh having different material and degradation characteristics are imported into BIM platforms thanks to visual programming algorithms implemented in Rhino’s Grasshopper. This step ensures the autonomous management and informatization of each class derived from segmentation, and the semantic data can be more easily shared, retrieved, visualized and stored, also in view of the use of heritage models for augmented reality applications.The results obtained in terms of description and semantic mapping of the model and of traceability and retrieval of information in H-BIM environment suggest the extension of the proposed methodological approach to the study of ornamental apparatuses related to other churches of the Carmelite order

Semantic Mapping of Architectural Heritage via Artificial Intelligence and H-BIM

Starting from the virtual photogrammetric 3D reconstruction, this work proposes a classification method, based on Artificial Intelligence, allowing to semi-automatically characterize the digital models of existing architectural heritage in terms of material mapping and/or decay condition. The obtained data, once classified, is used and transferred in BIM environment, so to favor the construction of informative models rich in analytical content. The proposed approach is described with reference to the significant case study of the Chiesa del Carmine in Pisa, for the study and restitution of the liturgical and decorative apparatus, as part of a large-scale research project, still underway, on the reconstruction of the tramezzo screens for the churches of the Mendicant orders

Alzheimer Caf\ue9: an approach focused on Alzheimer's patients but with remarkable values on the quality of life of their caregivers.

Background Alzheimer\u2019s disease (AD) affects the global quality of life of persons who suffer from it and their car- egivers, because of the behavioral and psychological conse- quences associated with the pathology and its caring. The Alzheimer Caf\ue9 (AC) is one example of approach aimed to help persons and caregivers deal with their disease. Aim This is a pilot study focusing on the efficacy of AC in relieving caregivers\u2019 and persons\u2019 burdens due to dementia. Methods The quality of life of both caregivers and persons who attended the AC was compared with the quality of life of those who did not. Basic and instrumental daily activities and neuropsychiatric functioning were assessed. Caregivers also answered to general well-being and caregiving burden questionnaires. The evaluation took place at the beginning of the intervention and after 1, 3, 6, 9 and 12 months. Results Caregivers who joined the AC with their persons with dementia showed to have significantly benefited in the daily care of persons with dementia, in terms of total well- being, vitality, and emotional burden. Discussion Although improvements were not observed in persons with dementia who attended the AC, significant benefits were reported by their caregivers, suggesting that the intervention may produce better management of social and economic problems and lead to better emotional support. Conclusions The AC seems to help families of AD persons to better manage the disease, and also delay the institution- alization of these persons, which is certainly an ambitious goal for an incurable disorder such as Alzheimer\u2019s disease

Reconstructing eruptive source parameters from tephra deposit: a numerical study of medium-sized explosive eruptions at Etna volcano

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