AI-Controlled Eigenstructure Detuning as a Unified Framework for Structural Life Extension

Abstract Fatigue-driven degradation and unwanted vibratory energy localization remain primary life-limiting mechanisms in large-scale engineering structures, particularly where fatigue-critical regions are inaccessible, weakly observable, or embedded within complex assemblies. Conventional mitigation strategies rely on passive design margins, inspection-driven maintenance, or localized repair, all of which act reactively after damaging stress pathways have already formed. This paper introduces AI-controlled eigenstructure detuning as a unified, system-level framework for structural life extension. Fatigue accumulation and energy radiation are interpreted as emergent consequences of persistent alignment between external excitation and a limited set of dominant eigenvectors, intrinsic deformation pathways that concentrate cyclic energy at welds, joints, and geometric discontinuities. Rather than targeting individual resonances, the proposed approach deliberately perturbs the structural stiffness operator to rotate the effective eigenstructure, redistributing dynamic energy away from critical regions without requiring direct access to them. A central contribution of this work is the formulation of an artificial intelligence layer as an adaptive eigenstructure controller. The AI does not predict damage directly, nor does it rely on explicit modal identification. Instead, it learns the evolving effective eigenstructure from sensor feedback and regulates localized ultrasonic or piezoelectric actuation in closed loop, maintaining detuned states under changing operational and environmental conditions. This enables continuous energy diffusion in systems exhibiting uncertainty, non-normal modal behavior, and time-varying dynamics. By integrating structural dynamics, active control, and AI-driven adaptation, the framework establishes a theoretical foundation for mitigating fatigue amplification and coherent energy localization in complex structures. The implications extend across multiple domains, including life extension of welded structures with inaccessible fatigue-critical details and control of vibration pathways responsible for efficient energy radiation, providing a scalable basis for risk reduction and long-term structural integrity.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-11
DOI
https://doi.org/10.5281/zenodo.18603530
Primary Topic
Aeroelasticity and Vibration Control
Type
article
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article

AI-Controlled Eigenstructure Detuning as a Unified Framework for Structural Life Extension

Luis Lopez Martinez
Zenodo (CERN European Organization for Nuclear Research)
Aeroelasticity and Vibration Control
article

AI-Controlled Eigenstructure Detuning as a Unified Framework for Structural Life Extension

Luis Lopez Martinez
article en

Abstract

Abstract Fatigue-driven degradation and unwanted vibratory energy localization remain primary life-limiting mechanisms in large-scale engineering structures, particularly where fatigue-critical regions are inaccessible, weakly observable, or embedded within complex assemblies. Conventional mitigation strategies rely on passive design margins, inspection-driven maintenance, or localized repair, all of which act reactively after damaging stress pathways have already formed. This paper introduces AI-controlled eigenstructure detuning as a unified, system-level framework for structural life extension. Fatigue accumulation and energy radiation are interpreted as emergent consequences of persistent alignment between external excitation and a limited set of dominant eigenvectors, intrinsic deformation pathways that concentrate cyclic energy at welds, joints, and geometric discontinuities. Rather than targeting individual resonances, the proposed approach deliberately perturbs the structural stiffness operator to rotate the effective eigenstructure, redistributing dynamic energy away from critical regions without requiring direct access to them. A central contribution of this work is the formulation of an artificial intelligence layer as an adaptive eigenstructure controller. The AI does not predict damage directly, nor does it rely on explicit modal identification. Instead, it learns the evolving effective eigenstructure from sensor feedback and regulates localized ultrasonic or piezoelectric actuation in closed loop, maintaining detuned states under changing operational and environmental conditions. This enables continuous energy diffusion in systems exhibiting uncertainty, non-normal modal behavior, and time-varying dynamics. By integrating structural dynamics, active control, and AI-driven adaptation, the framework establishes a theoretical foundation for mitigating fatigue amplification and coherent energy localization in complex structures. The implications extend across multiple domains, including life extension of welded structures with inaccessible fatigue-critical details and control of vibration pathways responsible for efficient energy radiation, providing a scalable basis for risk reduction and long-term structural integrity.

Zenodo (CERN European Organization for Nuclear Research)
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Aeroelasticity and Vibration Control
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AI-Controlled Eigenstructure Detuning as a Unified Framework for Structural Life Extension — Luis Lopez Martinez · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS