10 citations · 15 across the 12 of their papers we have counts for
13 papers
Physics-Informed Feature Fusion and Structural Metadata Integration for Transferable Post-Earthquake Damage Classification: Experimental Evaluation and Community-Recovery Implications
Huangbin Liang, Hanqing Zhang, Jiazeng Shan +1
Earthquake-induced structural damage assessment remains a key challenge for population-based Structural Health Monitoring (PBSHM), where damage representations must generalize acro…
PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
Marcus Haywood-Alexander, Gregory Duthé, Eleni Chatzi
Digital twins provide a powerful paradigm for diagnostic and prognostic tasks in the monitoring and control of engineered systems; however, their deployment for complex structures…
Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification
Xudong Jian, Charikleia Stoura, Simon Scandella +1
Damage identification is a core task in structural health monitoring. In practice, however, its reliability is often compromised by confounding non-damage effects, such as variatio…
A Mechanistic Analysis of Transformers for Dynamical Systems
Gregory Duthé, Nikolaos Evangelou, Wei Liu +2
Transformers are increasingly adopted for modeling and forecasting time-series, yet their internal mechanisms remain poorly understood from a dynamical systems perspective. In cont…
Causal Convolutional Neural Networks as Finite Impulse Response Filters
Kiran Bacsa, Wei Liu, Xudong Jian +2
This study investigates the behavior of Causal Convolutional Neural Networks (CNNs) with quasi-linear activation functions when applied to time-series data characterized by multimo…
A GAN-Based Framework for Generating STFT Spectrograms of Rare Acoustic Events in Structural Health Monitoring
Sasan Farhadi, Mariateresa Iavarone, Mauro Corrado +2
Structural Health Monitoring plays a crucial role in ensuring the safety, reliability, and longevity of bridge infrastructures through early damage detection. Although recent advan…
Quantifying the Value of Seismic Structural Health Monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement
Huangbin Liang, Beatriz Moya, Francisco Chinesta +1
Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspecti…
On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators
Wei Liu, Eleni Chatzi, Zhilu Lai
Kolmogorov-Arnold Networks (KANs) approximate multivariate functions by composing univariate transformations through additive or multiplicative aggregation. We establish convergenc…
A Multi-Model Probabilistic Framework for Seismic Risk Assessment and Retrofit Planning of Electric Power Networks
Huangbin Liang, Beatriz Moya, Francisco Chinesta +1
Electric power networks are critical lifelines, and their disruption during earthquakes can lead to severe cascading failures and significantly hinder post-disaster recovery. Enhan…
Higher-order transmissibility and its linear approximation for in-service crack identification in train wheelset axles
Ehsan Naghizadeh, Eleni Chatzi, Paolo Tiso
In-service structural health monitoring is a so far rarely exploited, yet potent option for early-stage crack detection and identification in train wheelset axles. This procedure i…
Automating Traffic Monitoring with SHM Sensor Networks via Vision-Supervised Deep Learning
Hanshuo Wu, Xudong Jian, Christos Lataniotis +3
Bridges, as critical components of civil infrastructure, are increasingly affected by deterioration, making reliable traffic monitoring essential for assessing their remaining serv…
Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
Wei Liu, Kiran Bacsa, Loon Ching Tang +1
Understanding and modeling nonlinear dynamical systems is a fundamental challenge across science and engineering. Deep learning has shown remarkable potential for capturing complex…
Resilience-based post disaster recovery optimization for infrastructure system via Deep Reinforcement Learning
Huangbin Liang, Beatriz Moya, Francisco Chinesta +1
Infrastructure systems are critical in modern communities but are highly susceptible to various natural and man-made disasters. Efficient post-disaster recovery requires repair-sch…