PSI - Issue 84

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 84 (2026) 433–440

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Conference Keywords: inspections; bridges; AI; defects; maintenance; assessment. As final output, CEA generates a report with the needed information for the engineer to assign the “Classe di Attenzione” (Attention Class), thus maximizing the platform’s readiness for the daily work of professionals. In addition, the recent development of the Maintenance Module provides engineers with quantitative data on areas of the bridge requiring intervention. These include crack injections, air/water jet and rebar cleaning, mortar application, protective coating, and other repairing operations. The framework is here demonstrated on a full-scale bridge located in Italy. Abstract This article demonstrates the application of the Civil Engineer Assistant (CEA) platform, developed by KnowCE SpA and adopted by RINA Consulting SpA. Starting from a 3D model CEA produces an enhanced version of the model, where defects are identified in compliance to the Italian Guidelines for bridge inspection and assessment (DM204/2022). The process starts by collecting and analyzing thousands of high-resolution images, taken as inputs for the generation of the 3D model. Artificial Intelligence algorithms drive the framework through identification, location, and analysis of defects; nevertheless, human supervision controls the output, leaving the validation process to domain experts. However, CEA enhances the cost effectiveness of the inspection process by automatically assigning k1, k2, G and the priority index to each defect. In addition, the platform can perform the structural assessment of each component of the asset as well as the entire assembly. III Fabre Conference: Existing Bridges, Viaducts, and Tunnels: Research, Innovation, and Applications From Digital Inspection to Digital Maintenance: how AI-driven tools can simplify maintenance and its planning Alessandro Pucci a , Claudia Gentile a , Domenico Galluccio a , Daniele Di Luca b , Davide Girardini b , Alessandro Donatelli b , Nicolò Spiezia b * a RINA Consulting S.p.A.,Via Cecchi, 6 - 16129 Genoa, Italy b KnowCE S.p.A, Foro Buonaparte, 55 - 20121 Milan, Italy

* Corresponding author. E-mail address: nicolospiezia@knowce.com

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Conference 10.1016/j.prostr.2026.06.056