Communications Engineering Selects Paper by CEE’s Simos Gerasimidis, Chengbo Ai, and Colleagues as “Editor’s Choice”
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For steel bridges, rust (which, as the saying goes, “never sleeps”) has historically led to catastrophic bridge failures, resulting in numerous fatalities and injuries. Now, one groundbreaking solution to that momentous issue has been signaled by the respected journal Communications Engineering, which has selected a collaborative paper published by Associate Professors Simos Gerasimidis and Chengbo Ai of the UMass Amherst Civil and Environmental Engineering (CEE) Department and five international colleagues for the distinguished status of “Editor’s Choice” for its 2024 issues. As the Communications Engineering editors summarized the trailblazing research, “Significantly, the approach replaces labor-intensive, subjective evaluations with a robust, automated system.”
The lead author of the paper was Georgios Tzortzinis from the Institute of Lightweight Engineering and Polymer Technology at Technische Universität Dresden in Germany and a recent graduate of the CEE Ph.D. program at UMass. The highlighted paper was originally published on August 1, 2024, in Communications Engineering.
In addition to three researchers from UMass Amherst – Gerasimidis, Ai, and CEE Ph.D. student Aidan Provost – the paper was written by international researchers Tzortzinis, Angelos Filippatos (Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, Greece), Jan Wittig (Department of Engineering Science, University of Oxford, Oxford, UK), and Maik Gude (Institute of Lightweight Engineering and Polymer Technology, Technische Universität Dresden).
As the Communications Engineering editorial team depicted the bridge-corrosion problem in the article introducing its 14 Editor’s Choice selections for 2024, “Currently, inspection practices rely heavily on subjective visual assessments and low-precision tools, resulting in labor-intensive processes that often lack consistency and accuracy. These traditional methods are further constrained by accessibility issues, time-intensive data collection, and an inability to provide detailed evaluations of corrosion patterns and their impacts on structural capacity.”
“To address these challenges,” as the Communications Engineering editorial staff explained, “Georgios Tzortzinis and colleagues presented…an innovative framework that combines 3D laser scanning and convolutional neural networks for precise evaluation of corroded steel bridges.”
According to the Communications Engineering editors, “The study integrates advanced point-cloud data from 3D-laser scanning with convolutional neural networks trained on over 1,400 corrosion scenarios, achieving remarkable classification and regression accuracy with errors as low as 2.0 percent and 3.3 percent, respectively. The method enables high-resolution visualization of corrosion profiles and accurate predictions of residual structural capacities. Validated on eight decommissioned girders and applied to an in-service bridge, the framework demonstrates transformative potential in bridge maintenance.”
As the paper’s seven authors concluded in their abstract, “This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches.”
The Gerasimidis Research Group (HOME | mysite) studies stability of structures and materials. As Gerasimidis said, “We are interested in numerical, analytical, and experimental methods to describe the stability of structural systems across scales. Our research interests lie in the areas of new truss or plate-lattice architected metamaterials, auxetic composites for civil infrastructure, shell-buckling and energy-barrier methods, analysis, inspection, and repairing of aging bridges, and energy structures.”
As Ai described his research group (AI Lab – A Transportation Research Group @ UMass Amherst), “Our lab dedicates its effort towards establishing a comprehensive, spatially-enabled transportation infrastructure and asset-data platform to better manage, support, and sustain the current and future transportation-infrastructure system via employing imaging, mobile LiDAR, and GPS/GIS technologies and developing computer-vision, ML/AI, and spatial-analysis methods.”
Communications Engineering is a selective, open-access journal from the celebrated Nature Portfolio, “publishing high-quality research, reviews, and commentary in all areas of engineering.” (February 2025)