Revealing the structural behaviour of Brunelleschi’s Dome with machine learning techniques

Abstract

The Brunelleschi’s Dome is one of the most iconic symbols 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 the start to a monitoring activity. In modern times, since 1988 a monitoring system comprised of 166 electronic sensors, including deformometers and thermometers, has been in operation, providing a valuable source of real-time data on the monument’s health status. With the deformometers taking measurements at least four times per day, a vast amount of data is now available to explore the potential of the latest Artificial Intelligence and Machine Learning techniques in the field of historical-architectural heritage conservation. The objective of this contribution is twofold. Firstly, 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 reduction techniques on the extensive daily detections generated by the monitoring system, while also accounting for fluctuations in temperature over time. Secondly, we estimate a number of recurrent and convolutional neural network models to verify their capability for medium- and long-term prediction of the structural evolution of the Dome. We believe this contribution is an important step forward in the protection and preservation of historical buildings, showing the utility of machine learning in a context in which these are still little used.