Machine Learning and Traditional Approaches in Shear Reliability of Steel Fiber Reinforced Concrete Beams

Aug 4, 2026

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In structural engineering, steel fibre reinforced concrete (SFRC) beams are increasingly studied for improved tension and shear performance. This work presents a reliability analysis of shear-capacity predictions, organizing 142 high-strength (HSFRC) and 265 normal-strength (NSFRC) beams and evaluating how shear design models behave under uncertainty. It tests the adequacy of the standard Gaussian assumption and proposes Lognormal and Weibull alternatives. Using FORM and SORM across dead, live, snow, wind and seismic loads, it compares empirical, semi-empirical and machine-learning formulas, analyzes risk-level resistance coefficients, and finds traditional equations often conservative while ML formulas offer better prediction, with reliability balanced against safety and cost.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Abstract

Keywords

Introduction

Reliability framework and uncertainty modeling

Load cases and model comparison

Risk-level resistance coefficients

Results and sensitivity analysis

University of Birmingham Machine Learning and Traditional Approaches in Shear Reliability of Steel Fiber Reinforced Concrete Beams Qin, Xia; Kaewunruen, Sakdirat DOI: 10.1016/j.ress.2024.110339 License: Creative Commons: Attribution (CC BY) Document Version Publisher's PDF, also known as Version of record Citation for published version (Harvard): Qin, X & Kaewunruen, S 2024, 'Machine Learning and Traditional Approaches in Shear Reliability of Steel Fiber Reinforced Concrete Beams', Reliability Engineering and System Safety, vol. 251, 110339. [https://doi.org/10.1016/j.ress.2024.110339](https://doi.org/10.1016/j.ress.2024.110339) Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 03. Aug. 2026 Reliability Engineering and System Safety 251 (2024) 110339 Contents lists available at ScienceDirect Reliability Engineering and System Safety journal [homepage: www.elsevier.com/locate/ress](homepage: www.elsevier.com/locate/ress) | Machine learning and traditional approaches in shear reliability of steel fiber reinforced concrete beams
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Xia Qin , Sakdirat Kaewunruen
Department of Civil Engineering, School of Engineering, University of Birmingham, Edgbaston B15 2TT, UK | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Steel fibre reinforced concrete beams Reliability analysis
Uncertainty analysis Sensitivity analysis Structure design | | In the field of structural engineering, the exploration of steel fibre reinforced concrete (SFRC) beams has recently intensified, particularly due to their improved tension and shear performance of structure. This study pioneers a novel reliability analysis of shear capacity predictions for SFRC beams, distinctively classifying the datasets into high-strength (HSFRC) and normal-strength (NSFRC) categories. A comprehensive database of 142 HSFRC and 265 NSFRC beams serves as the foundation for this analysis, which critically examines the standard Gaussian distribution in shear design models and proposes the Lognormal and Weibull distributions as more precise alternatives. Employing advanced First-order (FORM) and Second-order Reliability Methods (SORM), the study covers a broad spectrum of load conditions, including dead, live, snow, wind, and seismic loads, to evaluate various empirical, semi-empirical and machine learning proposed shear capacity prediction formulas. One of the key innovations of this research is the development of differentiated resistance coefficients for various

FAQs

What is the main goal of the study on SFRC beam shear reliability?

The study aims to improve shear-capacity prediction reliability by analyzing uncertainty and evaluating multiple prediction formula families for steel fibre reinforced concrete beams.

How are high-strength and normal-strength SFRC beams handled in the analysis?

The dataset is differentiated into high-strength (HSFRC) and normal-strength (NSFRC) categories, using 142 HSFRC and 265 NSFRC beams as the basis for the reliability evaluation.

What reliability methods and load conditions are used to assess shear predictions?

First-order (FORM) and second-order reliability methods (SORM) are applied across dead, live, snow, wind, and seismic loads to evaluate empirical, semi-empirical, and machine-learning shear-capacity formulas.

Aug 4, 2026

Upload by Aurora