Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

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Researchers have invested much effort into developing kinetic models due to their ability to capture dynamic behaviors, transient states, and regulatory mechanisms of metabolism, providing a detailed and realistic representation of cellular processes. Hist ...

We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning app ...

Natural aerosol components such as particulate methanesulfonic acid (MSA p) play an important role in the Arctic climate. However, numerical models struggle to reproduce MSA p concentrations and seasonality. Here we present an alternative data-driven metho ...

Most codes of practice adopt a semi probabilistic design approach for the dimensioning and assessment of structures. Accordingly, structural safety is ensured by performing limit state verifications using design values determined with adequately calibrated ...

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 ...

MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into map ...

Reinforced concrete flat slabs consist of a continuous, thin concrete plate that rests on a grid of columns. The supporting surface of the columns is very small compared to the floor plan dimensions, leading to concentrations of shear forces near the colum ...

Evaluating the visual quality of distorted images is a crucial task in the field of image compression, as artifacts may significantly impair the appeal and fidelity of images, therefore reducing the user experience. As assessing the visual quality of image ...

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

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 ...

Bond between reinforcing bars and concrete has been the focus of extensive research over the last century. This is well-justified as the functioning of reinforced concrete intimately depends on the interaction between rebar and concrete, as for example cra ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Estimating the stress of reinforcing bars and its variations in service conditions can be useful to determine the reserve capacity of structures or to assess the risk of fatigue in the reinforcement. This paper investigates the use crack width measurements ...

Codes of practice can be overly conservative, particularly for the shear resistance of reinforced concrete beams with shear reinforcement when large loads act close to supports. This thesis addresses the topic by proposing a refined design approach based o ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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 ...

A large part of building demolitions is motivated by purely socio-economic reasons. Hence, about-to-be-demolished structures, commonly made of reinforced concrete, very often present no or little degradation. When adaptive reuse of the entire building is n ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

Carefully extracting reinforced concrete (RC) elements from soon-to-be demolished structures and reusing them as load-bearing components is an emerging circular low-carbon alternative to building new structures. As floor construction typically accounts for ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

Chat with Graph Search

The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games

Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations

Influence of model uncertainty and long term deformations in action effects calculation in reinforced concrete structures

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

MapPool -Bubbling up an extremely large corpus of maps for AI

Shear and steel-fibre reinforcement for the punching resistance of flat slabs at internal and edge columns

Benchmarking conventional and learning-based objective image quality metrics on compression artifacts

Mesh-based topology, shape and sizing optimization of ribbed plates

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

Contributions to rebar-to-concrete interaction and its structural implications for design and monitoring applications

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Estimation of the bar stress based on crack width measurements in reinforced concrete structures

Experimental and analytical investigation of shear-reinforced concrete and dowel action

Understanding generalization and robustness in modern deep learning

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

Reusability assessment of reinforced concrete components prior to deconstruction from obsolete buildings

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

FLO:RE – A new floor system made of reused reinforced concrete and steel elements

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation