Deep-Learning-Informed Design Scheme for Prediction of Interfacial Concrete Shear Strength

Abstract

Current design provisions pertaining to the shear transfer strength of concrete-to-concrete interfaces, including those of the AASHTO LRFD design specifications and ACI 318 Code, are based on limited physical test data from studies conducted decades ago. Since the development of these design provisions, many studies have been conducted to investigate additional parameters. In addition, modern concrete technology has expanded the range of materials available and often includes the use of high-strength concrete and high-strength reinforcing steel. Recent studies examined the applicability of current shear-friction design approaches to interfaces that comprise high-strength concrete and/or high-strength steel and identified a need for revision to the existing provisions. To this end, this study leveraged a comprehensive database of test results collected from the literature to propose a deep-learning-based predictive model for normalweight concrete-to-concrete interfacial shear strength. Additionally, a new computation scheme is proposed to estimate the nominal shear strength with a higher prediction accuracy than the existing AASHTO LRFD and ACI 318 design provisions.

Department(s)

Civil, Architectural and Environmental Engineering

Comments

University of Nebraska-Lincoln, Grant 00065573

Keywords and Phrases

deep learning; interfacial shear strength; learning-informed design; neural additive models; neural network; reinforced concrete; shear friction

International Standard Serial Number (ISSN)

0889-3241

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 American Concrete Institute, All rights reserved.

Publication Date

01 Jan 2025

Link to Full Text

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