Title:
Evaluation of the KDS 14 Draft Design Method for Predicting the Shear Strength of Prestressed Concrete Beams
Author(s):
Ngoc Hieu Dinh, Si-Hyun Kim and Kyoung-Kyu Choi
Publication:
IJCSM
Volume:
19
Issue:
Appears on pages(s):
Keywords:
Shear strength, Prestressed concrete, Concrete beam, Compression zone, Stirrups
DOI:
10.1186/s40069-025-00794-0
Date:
9/30/2025
Abstract:
One-way shear strength evaluation is one of the essential and complex aspects in the design of prestressed concrete (PSC) members. Current design standards adopt different empirical or semi-empirical approaches to predict the one-way shear strength of PSC members. This study evaluated the applicability of the KDS 14 draft design method, which is based on compression zone failure theory, for predicting the shear strength of slender PSC beams. The evaluation utilized the ACI-DAfStb database, comprising 331 one-way shear tests on PSC beams with and without shear reinforcement. The strength prediction of the KDS 14 draft model was compared against those of existing design codes and design-oriented models. Results indicated that the KDS 14 draft model demonstrated promising performance in predicting the shear strength of a large dataset of PSC beams, both with and without stirrups. For PSC beams without stirrups, the KDS 14 draft model exhibited better accuracy with less scatteredness compared to the ACI 318-19 and CSA A23.3:24 models, while maintaining design conservatism. For PSC beams with stirrups, the KDS 14 draft model showed predictive performance comparable to the CSA A23.3:24 model. In addition, the KDS model exhibits similar scatteredness compared to the mechanics-based model proposed Marí et al. but while providing more conservative predictions. Furthermore, parametric study and design example were conducted to understand the influence of key design parameters and the applicability of the KDS 14 draft model for PSC beams. Overall, the predictions by the KDS 14 draft model closely aligned with trends observed in experimental results across most scenarios.