首页|Reports from University of Florence Provide New Insights into Machine Learning ( Revealing the Structural Behaviour of Brunelleschi's Dome With Machine Learning Techniques)

Reports from University of Florence Provide New Insights into Machine Learning ( Revealing the Structural Behaviour of Brunelleschi's Dome With Machine Learning Techniques)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Florence, Italy, by NewsRx e ditors, research stated, "The Brunelleschi's Dome is one of the most iconic symb ols of the Renaissance and is among the largest masonry domes ever constructed. Since the late 17th century, first masonry cracks appeared on the Dome, giving t he start to a monitoring activity." Financial supporters for this research include Universita degli Studi di Firenze within the CRUI-CARE Agreement, National Research Center in High Performance Co mputing, Big Data and Quantum Computing foreseen within Mission 4 (Education and Research) of the "National Recovery and Resilience Plan" (NRRP), Next Generatio n EU (NGEU) program. Our news journalists obtained a quote from the research from the University of F lorence, "In modern times, since 1988 a monitoring system comprised of 166 elect ronic sensors, including deformometers and thermometers, has been in operation, providing a valuable source of real-time data on the monument's health status. W ith the deformometers taking measurements at least four times per day, a vast am ount of data is now available to explore the potential of the latest Artificial Intelligence and Machine Learning techniques in the field of historical-architec tural heritage conservation. The objective of this contribution is twofold. Firs tly, for the first time ever, we aim to unveil the overall structural behaviour of the Dome as a whole, as well as that of its specific sections (known as webs) . We achieve this by evaluating the effectiveness of certain dimensionality redu ction techniques on the extensive daily detections generated by the monitoring s ystem, while also accounting for fluctuations in temperature over time. Secondly , we estimate a number of recurrent and convolutional neural network models to v erify their capability for medium- and long-term prediction of the structural ev olution of the Dome."

FlorenceItalyEuropeCyborgsEmergi ng TechnologiesMachine LearningUniversity of Florence

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.6)