Browsing by Subject "Digital twin"

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Open Access
Digital twin-based approach for thermal efficiency assessment and optimization in the Nigerian construction industry
(2024-06) Haliru, Ibrahim Tahir
The Nigerian construction industry has been experiencing rapid growth over the years. The country's increasing population, urbanization, and economic growth have created a high demand for construction. Abuja, the capital city and administrative hub of Nigeria, has attracted substantial investment in infrastructural development, spanning commercial and residential projects. Nonetheless, substandard practices, inadequate regulations, and disregard for sustainable practices have been plaguing the industry. Therefore, with the aim of identifying recurrent challenges and constraints associated with the adoption of sustainable construction practices in Abuja, this study analyzes the current state of the construction industry, its approach, prioritization, and implementation towards sustainability. Through semi-structured interviews with resident architects, relevant insights were attained, and 18 factors from 8 distinct categories emerge as contributing challenges impeding the progression, adoption, and implementation of sustainable practices in the industry. Furthermore, through the implementation of Digital Twin technology, this study observes and analyzes the real-time thermal performance of a standard residential duplex in Abuja for a duration of fifteen days. Based on the real-time visual feedback and analysis, an exceptionally low amount of daylight illuminance and exceedingly high indoor temperatures were observed. As such, design recommendations and strategies that seek to achieve 3 primary objectives, i.e., minimize solar radiation, maximize daylighting, and effectively utilize efficient passive cooling strategies, are proposed.
Open Access
The impact of virtual tours on museum exhibitions after the onset of covid-19 restrictions: visitor engagement and long-term perspectives
(Centro Euromediterraneo di Innovazione Tecnologica per i Beni Culturali e Ambientali e la Biomedicina, 2021) Resta, Giuseppe; Dicuonzo, F.; Karacan, Evrim; Pastore, D.
After the outbreak of Covid-19, galleries and museums have been experimenting with new ways to engage a potential audience remotely. This study focuses on the level of engagement of virtual tours in museums looking at the representation of architectural space, representation artifacts, and ease of use as possible correlated factors. A sample group of eighty early-career experts in the field of art, architecture, or design assessed their visit to the archaeological museum of Troya Müzesi in Çanakkale, Turkey; half of the participants resided in Turkey, while the other half in Italy. This paper has addressed the following research questions with an online multi-level study: how is the online exhibition platform evaluated by its audience? Can regular employment of virtual tours engage new visitors in the long term? Is the representation of a museum, in the form of a virtual twin, an adequate surrogate that creates an immersive visiting experience?
Open Access
A workflow for synthetic data generation and predictive maintenance for vibration data
(Molecular Diversity Preservation International (MDPI), 2021-09-22) Selçuk, Şahan Yoruç; Ünal, Perin; Albayrak, Özlem; Jomâa, Moez
Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area.