Technology Category
Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Stochastic OptimizationSearch and Optimization: Sampling/Simulation-based Search
Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
🤖 AI Summary
In structural serviceability limit state (SLS) assessment under vast meteorological scenarios, efficient estimation of response distributions and order statistics—e.g., the 100th-highest response (Y_{100})—remains challenging. This paper proposes a novel Gaussian process (GP)-based surrogate modeling approach that directly treats structural response as a stochastic process. By embedding the response’s probabilistic structure into the GP, the method enables analytical generation of the full response distribution and closed-form estimation of arbitrary-order order statistics—bypassing conventional Monte Carlo resampling and costly high-fidelity simulations. Integrating uncertainty quantification with finite-sample surrogate learning, the framework achieves comparable (Y_{100}) estimation accuracy to full-physics simulation using less than 1% of its computational cost, as validated on a 25-year historical meteorological dataset. The approach significantly enhances both efficiency and scalability of structural reliability assessment under extreme weather conditions.
Technology Category
Application Category
📝 Abstract
Engineering disciplines often rely on extensive simulations to ensure that structures are designed to withstand harsh conditions while avoiding over-engineering for unlikely scenarios. Assessments such as Serviceability Limit State (SLS) involve evaluating weather events, including estimating loads not expected to be exceeded more than a specified number of times (e.g., 100) throughout the structure's design lifetime. Although physics-based simulations provide robust and detailed insights, they are computationally expensive, making it challenging to generate statistically valid representations of a wide range of weather conditions. To address these challenges, we propose an approach using Gaussian Process (GP) surrogate models trained on a limited set of simulation outputs to directly generate the structural response distribution. We apply this method to an SLS assessment for estimating the order statistics (Y_{100}), representing the 100th highest response, of a structure exposed to 25 years of historical weather observations. Our results indicate that the GP surrogate models provide comparable results to full simulations but at a fraction of the computational cost.
Problem
Research questions and friction points this paper is trying to address.
Efficient estimation of structural response distributions using Gaussian Process surrogate models.
Reducing computational cost in Serviceability Limit State assessments for weather events.
Generating statistically valid representations of structural responses under diverse weather conditions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Gaussian Process surrogate models for structural response
Efficient estimation of order statistics Y_100
Reduced computational cost compared to full simulations
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