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Condition monitoring is traditionally performed through the analysis of data collected during visual inspections or recorded by network of sensors installed on the monitored structure. Remote sensing using multiple drones (multicopters), highly automated, fed by tethers to ensure long flight-time, and equipped with several sensors, may provide an effective improvement enabling he acquisition of large amounts of 3D, multi-temporal, multi-spectral, and multi-scale data. The research will develop a multi-drone supported condition assessment tool that will exploit such data to assess the health of critical and cultural heritage constructions. This will entail:
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• Definition and first prototype realization of the system layout and data collection procedure (type of data, frequency of surveys, optimal planning of monitoring missions).
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• Development of the techniques for their effective processing using big data analytics and machine learning techniques.
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• Experimental application.
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; The data will be acquired with the developed prototype, and the processing techniques will be developed with the expertise of DABC (Department of Architecture, Built environment and Construction engineering). The research entails multidisciplinary methods ranging from machine learning based on big data for condition monitoring in civil engineering, to control and robotics, mechanical engineering, and power conversion.