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Corrosion morphology prediction for civil infrastructure using physics-based simulation is computationally challenging due to the coupled multi-physics simulations involved in long-term corrosion prognostics. Machine learning (ML)-based surrogate modeling provides a promising way of overcoming this challenge. This paper presents a physics-constrained ML method for surrogate modeling of a high-fidelity multi-physics Read more…

Our paper on fusion of CAE data and test data for vehicle crashworthiness performance evaluation by analysis got published in SMO Journal. This is a collaborative work with researchers from Ford Motor Company. Get the paper here.

Yixuan Liu successfully defended his Ph.D. dissertation entitled “Model-Based Reliable Mission Planning of Off-Road Autonomous Ground Vehicles Under Uncertain Environments“. Congratulations!!

Unmanned Aerial Vehicle (UAV)-based autonomous damage inspection has become a promising technique to replace certain human-performed inspections due to both safety and cost considerations. While current UAV-based damage inspection methods have been successful in minimizing the length of the flying path considering potential defect locations, the impact of flying path Read more…

Selective laser melting (SLM) is a commonly used technique in additive manufacturing to produce metal components with complex geometries and high precision. However, the poor process reproducibility and unstable product reliability has hindered its wide adoption in practice. Hence, there is a pressing demand for in-situ quality monitoring and real-time Read more…

Automated operational modal analysis is essential for online structural health monitoring without human intervention. It remains a challenging issue due to the need of processing a large number of datasets and the involvement of many user-specified thresholds. This paper proposes a novel automated modal identification approach based on stochastic subspace Read more…

Get the paper here. Bayesian inference plays a vital role in Structural Health Monitoring (SHM) by assessing structural integrity through probabilistic model updating using monitoring data. A crucial component in Bayesian inference is the evaluation of the likelihood function. For some situations, the likelihood function is not available in closed Read more…

Our two-part review paper on Digital Twins in collaboration with leading researchers in this field got accepted by Structural and Multidisciplinary Optimization Journal. Get the papers here and here.

Papers on Bayesian model updating and Failure Prognostics got published in Engineering Structures and Structural and Multidisciplinary Optimization. Get the papers here and here.