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New AI model predicts bridge structural responses with 60x speedup · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an adaptive-trunk DeepONet model to improve the prediction of localized structural responses in long-span roadway bridges. This new framework uses a k-nearest neighbors (KNN) strategy to dynamically create a load-dependent learning domain, enabling the network to focus on critical structural influence zones. The model incorporates distance-aware features and a physics-based reconstruction method, achieving FEM-level accuracy with significantly reduced computational time, up to 60x faster overall and four orders of magnitude faster for inference alone. AI

IMPACT This AI model significantly accelerates structural analysis for bridges, enabling faster digital twin applications and infrastructure assessment.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new AI model for structural analysis.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI model predicts bridge structural responses with 60x speedup · 2 sources tracked

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Research
The cluster contains a research paper published on arXiv detailing a new AI model for structural analysis.
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2 independent sources
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paper, infra
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High
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145 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly, Tarek Abdoun, Mostafa E. Mobasher ·

    Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges

    arXiv:2606.20015v1 Announce Type: new Abstract: Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twin…

  2. arXiv cs.LG TIER_1 English(EN) · Mostafa E. Mobasher ·

    Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges

    Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twins. Existing SciML approaches struggle to accurat…

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