Full and Semi-Probabilistic Analysis of Ultra-High-Performance Concrete Beams in Bending via Machine Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00385805" target="_blank" >RIV/68407700:21110/25:00385805 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21610/25:00385805

Result on the web

<a href="https://doi.org/10.1080/10168664.2025.2500458" target="_blank" >https://doi.org/10.1080/10168664.2025.2500458</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10168664.2025.2500458" target="_blank" >10.1080/10168664.2025.2500458</a>

Result language

angličtina

Original language name

Full and Semi-Probabilistic Analysis of Ultra-High-Performance Concrete Beams in Bending via Machine Learning

Original language description

Ultra-High-Performance Concrete (UHPC) offers exceptional mechanical properties, yielding it an attractive choice for advanced structural engineering applications. However, predicting the bending capacity of UHPC beams may remain a challenge due to its complex physical behavior and inherent variability. This study integrates a machine learning (ML) model based on Extreme Gradient Boosting (XGBoost) algorithm, with probabilistic analysis to enhance predictive accuracy and reliability. A database of 187 UHPC beams is collected and used to develop the XGBoost model, achieving superior performance metrics compared to traditional analytical methods like the Federal Highway Administration (FHWA) and the Swiss Standard (SIA 2052). Model uncertainty is evaluated, and a global resistance factor ($gamma _R, = , 1.35$gamma R=1.35) is derived from semi-probabilistic design. Fully probabilistic reliability analyses using Monte Carlo simulations demonstrate the efficacy of XGBoost in achieving target reliability indices for diverse beam configurations. Reliability indices for UHPC beams designed with XGBoost range from 3.0 to 4.3 depending on the steel reinforcement ratio, with lightly reinforced beams averaging the optimal value of 3.8, reflecting a balanced cost-safety design. In contrast, heavily steel-reinforced beams consistently exceed this optimal value, indicating potential overdesign under the semi-probabilistic approach. Additionally, SHAP (Shapley Additive Explanations) analyses provide insights into the influence of key variables when predicting with XGBoost, emphasizing the importance of reinforcement ratios and effective depths. The proposed framework facilitates robust and efficient design of UHPC beams with reduced model uncertainties and corresponding global resistance factors preferred for structural engineering practices.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20102 - Construction engineering, Municipal and structural engineering

Project

<a href="/en/project/GA24-10892S" target="_blank" >GA24-10892S: Machine Learning for Multiscale Modelling of Spatial Variability and Fracture for Sustainable Concrete Structures</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Structural Engineering International

ISSN

1016-8664

e-ISSN

1683-0350

Volume of the periodical

35

Issue of the periodical within the volume

4

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

634-648

UT code for WoS article

001510634800001

EID of the result in the Scopus database

2-s2.0-105008354012