Data-driven analysis models for slender structures using explainable artificial intelligence
This project aims to study dynamic behavior of slender structures using structural health monitoring (SHM) data. An explainable artificial intelligence (XAI) approach, specifically an explainable machine learning approach, is proposed as a conceptual basis. A suite of machine learning techniques will be created that, compared to many “traditional” machine learning approaches, produce more explainable models, while maintaining high learning performance and prediction accuracy. As opposed to “black box” models inherent to many traditional machine learning approaches employed in civil and environmental engineering, so-called "glass box" models will be introduced that are explainable to engineers in practice. The machine learning techniques, validated in preliminary work, will focus on specific aspects of the dynamic behavior of slender structures, which have proven responsible for causing excessive oscillations. SHM data recorded from a relay tower and from a pedestrian bridge, studied by the German applicant and by the Greek collaborating partner, respectively, will be analyzed on a data-driven basis without underlying physical principles a priori considered, in an attempt to complement knowledge derived from theory on the dynamic behavior of slender structures, thus creating a holistic view on the performance of slender structures subjected to dynamic loads. For example, resonant phenomena between wind loads and the structural response of towers (vortex-induced vibrations), flutter, galloping, or the interaction between the dynamic loads and the structural response (“lock-in” effect) will be investigated. It is expected that the XAI approach will enable engineers to (better) understand, appropriately trust, and effectively manage the generation of artificially intelligent slender structure analyses in engineering practice.
This project is an international collaboration that serves to establish collaborative relationships between international partners, and funding has been provided for (i) exploratory workshop, (ii) trips abroad, and (iii) guest visits. The purpose of an exploratory, generally bilateral, workshop is to prepare a specific joint project or to explore possibilities of specific, topic-related collaboration, while trips abroad and guest visits serve to facilitate the preparation of a specific joint project. A specific joint project that has emerged out of this international collaboration is the project entitled "Resilient infrastructure based on cognitive buildings", funded by the German Research Foundation.
Professor Dr. Kay Smarsly
Hamburg University of Technology
Institute of Digital and Autonomous Construction
Professor George D. Manolis, PhD.
Aristotle University of Thessaloniki
Department of Civil Engineering
Division of Structures