In recent times, there has been an increasing prevalence of surrogate models and
metamodeling techniques in approximating the responses of complex systems. These …

Bridges are subject to a plethora of deterioration phenomena, such as corrosion, fatigue,
and damaging events (eg, truck impacts and earthquakes) that can affect their performance …

The digital twin concept represents an appealing opportunity to advance condition-based
and predictive maintenance paradigms for civil engineering systems, thus allowing reduced …

Damage detection in bridge structures has always been challenging, particularly for long‐
span bridges with complex structural forms. In this study, a partial‐model‐based damage …

High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted
computational budget can significantly limit the number of parameter configurations …

Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

An approach based on a Physics-Informed Neural Network (PINN) is introduced to tackle the
two-dimensional (2D) rheological consolidation problem in the soil surrounding twin tunnels …

The current challenges facing data-driven impact localisation methods primarily involve
accurately localising impacts occurring outside the training impact coverage area …

This paper addresses the computational challenges inherent in the stochastic
characterization and uncertainty quantification of Micro-Electro-Mechanical Systems …

This article demonstrates a parameter estimation technique for bioprocesses that utilizes
measurement noise in experimental data to determine credible intervals on parameter …