Publications associées à Auto-tuning ensemble models for estimating shear resistance of headed studs in concrete

Publications associées (39)

TimeGPT in load forecasting: A large time series model perspective

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

2025

Ensemble learning for portfolio valuation and risk management

Damir Filipovic, Lotfi Boudabsa

We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2025

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Eklavya Sarkar

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

EPFL2025

Musculoskeletal motor control with reinforcement learning

Alberto Silvio Chiappa

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. This thesis presents a collection of ...

EPFL2025

New insights into the reaction mechanisms of calcium-based activators modified sodium carbonated activated slag: Precursor dissolution and carboaluminate generation

Lei Xu

This study investigated the regulatory mechanism of calcium-based activators on the reaction kinetics of sodium carbonate-activated slag (SAS), with the aim of revisiting the conventional interpretation based on liquid-phase alkalinity enhancement. By inco ...

2025

Teaching oxidation states to neural networks

Nicola Marzari

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with st ...

2025

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Xin Chen

Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequ ...

Wiley2025

Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

Olga Fink, Ismail Nejjar

The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

2025

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

Ahmad Abdulrahman Ahmad Majdouba

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 ...

Springer2024

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

Corentin Jean Dominique Fivet, Numa Joy Bertola, Célia Marine Küpfer

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 ...

2024

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

Arnout Jan J Devos

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 ...

EPFL2024

Engineering novel protein interactions with therapeutic potential using deep learning-guided surface design

Anthony Marchand

Proteins are foundational biomolecules of life playing a crucial role in a myriad of biological processes. Their function often requires interplay with other biomolecules, including proteins themselves. Protein-protein interactions (PPIs) are essential for ...

EPFL2024

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

Aurelio Muttoni, Alain Nussbaumer, Xhemsi Malja

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 ...

Ernst & Sohn2024

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

Xhemsi Malja

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 ...

EPFL2024

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer Nature2024

Masonry walls from reclaimed concrete demolition waste

Jakov Oreb, Katrin Beyer, Igor Tomic

The construction sector is the largest consumer of non-renewable resources and the most significant contributor to CO2 emissions. Reusing entire components or reclaiming their constituent parts, instead of recycling structural elements at the material leve ...

EDP Sciences2024

Rising from rubble - Leveraging existing construction tools for upcycling concrete waste into slender walls

Katrin Beyer, Corentin Jean Dominique Fivet, Stefana Parascho, Maxence Grangeot, Qianqing Wang

In this paper, we present a new method for upcycling concrete rubble waste into slender walls through the lightweight digital augmentation of mainstream construction machines. By using such method, the environmental impact of concrete construction and demo ...

Springer2024

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Lakmal Buddika Meegahapola

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

EPFL2024

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

Corentin Jean Dominique Fivet, Maléna Bastien Masse, Julie Rachel Devènes

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 ...

2024

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

Olga Fink, Hao Lu

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 ...

2024