Model Falsification for Predicting Dynamical Responses of Uncertain Structural Systems

2025-10-02

AI Summary

Predicting the dynamic response of structural systems faces challenges arising from a large candidate model space and uncertain model parameters. This paper proposes an approximate Bayesian prediction framework grounded in model falsification: it introduces a likelihood threshold controlled by the false discovery rate (FDR) to efficiently eliminate candidate models incapable of reproducing observed structural responses; Bayesian prediction is then performed only over the non-falsified subset, with weights assigned accordingly. By integrating model falsification theory, Bayesian inference, and dynamical systems modeling, the method substantially reduces computational cost. Evaluated on three canonical structural systems, the approach maintains high predictive accuracy while reducing the number of required model simulations by over 70%, demonstrating its effectiveness, robustness, and computational efficiency.

Technology Category

• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate prediction of dynamical response of structural system depends on the correct modeling of that system. However, modeling becomes increasingly challenging when there are many candidate models available to describe the system behavior. Furthermore, uncertainties can be present even for the parameters of these model classes. The plausibility of each input-output model class of the structures with uncertain components can be determined by a Bayesian approach from measured dynamic responses to one or more input records; predictions of the structural system response to alternate input records can then be made. However, this approach may require many model simulations, even though most of those model classes are quite implausible. An approach is proposed herein to use a bound, computed from the false discovery rate, on the likelihood of measured data to falsify models considering uncertainties in the passive control devices that do not reproduce the measured data to sufficient accuracy. Response prediction is then performed using the unfalsified models in an approximate Bayesian sense by assigning weights, computed from the likelihoods, only to the unfalsified models approach incurring only a fraction of the computational cost of the standard Bayesian approach. The proposed approach for response prediction is illustrated using three structural examples: an earthquake-excited four--degree-of-freedom building model with a hysteretic isolation layer; a 1623--degree-of-freedom three-dimensional building model, with tuned mass dampers attached to its roof, subjected to wind loads; and a full-scale four-story base-isolated building tested on world's largest shake table in Japan's E-Defense lab. The results exhibit accurate response predictions and significant computational savings, thereby illustrating the potential of the proposed method.

Problem

• Falsifying implausible models for uncertain structural systems
• Reducing computational cost in Bayesian response prediction
• Validating method on buildings with passive control devices

Innovation

• Model falsification using false discovery rate bounds
• Computational cost reduction via approximate Bayesian weighting
• Application to structural systems with uncertain components