Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

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This data package supports the publication 'Complexity of crack front geometry enhances toughness of brittle solids' by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski Nature physics (2024) - https://doi.org/10.1038/s41567-024-02435-x DOI: 1 ...

Context. The cosmological surveys that are planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Univ ...

Examining the performance of an existing bridge requires information on several aspects, such as design choices, material properties, and ongoing degradation processes. It often happens that some critical information is not available. The traditional appro ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

Suspended epitaxial gallium nitride (GaN) on silicon (Si) photonic crystal devices suffer from large residual tensile strain, especially for long waveguides, because fine structures tend to crack due to large stress. By introducing spring-like tethers, des ...

The study explores an original idea that responds to the urgent need to reduce the detrimental environmental impacts of load-bearing floor construction in new buildings by reusing saw-cut reinforced concrete (RC) pieces salvaged from soon-to-be demolished ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

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Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"

XXXIV. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear

A global framework for data-informed bridge examination

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Strain engineering and strain measurement by spring tethers on suspended epitaxial GaN-on-Si photonic crystal devices

Reuse of cut concrete slabs in new buildings for circular ultra-low-carbon floor designs

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Spectral Estimators for High-Dimensional Matrix Inference

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Random matrix methods for high-dimensional machine learning models