Digitalization of the Workflow for Drone-Assisted Inspection and Automated Assessment of Industrial Buildings for Effective Maintenance Management

Industrial buildings are a key element in the industrial fabric, and their maintenance is essential to ensure their proper functioning and avoid disruptions and costly economic losses. Continuous maintenance based on an accurate diagnosis makes it possible to meet the challenges of aging infrastructures, which demands a reliable data-based assessment for maintenance management implementing corrective and preventive actions, according to the damage criticality. This paper researches an innovative digitalized process for the inspection and diagnosis of industrial buildings, which leads to categorizing and prioritizing maintenance actions in an objective and cost-effective way from the inspection data. The process integrates some technical developments carried out in this work, aimed to automate the workflow: the drone-based inspection, the building condition assessment from the definition of a standardized construction pathology library, and a visual analysis of pathology evolution based on photogrammetry. The use of drones for digitalized inspection involves some challenges related to the positioning of the drone for damage localization, which has been herein overcome by developing a geo-annotation system for image acquisition. This system has also enabled the capture of geo-located images intended to generate 3D photogrammetric models for quantifying the pathological process evolution. Moreover, the assessment procedure outlined through multi-criteria decision-making methodology MIVES establishes a single criterion to automatically weight the relative importance of the damage defined in the library. As a result, this procedure yields the so-called Intervention Urgency Index (IUI), which allows prioritizing the maintenance actions associated with the damage while also considering economic criteria. In such a way, the overall process aims to increase reliability and consistency in the results of inspection and diagnosis needed for the effective maintenance management of industrial buildings.

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