Extracting Knowledge of Concrete Shear Strength from Artificial Neural Networks

Authors

  • William A Young Ohio University
  • Gary R Weckman Ohio University
  • Michael D Brown Ohio University
  • Jim Thompson Ohio University

DOI:

https://doi.org/10.23055/ijietap.2008.15.1.59

Keywords:

Concrete shear strength, artificial neural networks, knowledge extraction, Levenberg-Marquardt learning algorithm, generalized feed-forward network

Abstract

This article introduces an artificial neural network (ANN) to estimate the shear strength of reinforced concrete beams. Current methods for calculating shear strength use a model that is based on engineering mechanics and empirical values determined though testing of a beam failing due to shear. The current methods are intended to provide a conservative lower bound on the strength needed to prevent a shear failure. A database containing the results of over 1200 laboratory shear strength tests was used to train an ANN. The database contained the geometric and material property data from the test specimens and the recorded failure load. The ANN presented in this paper was able to predict the shear strength of reinforced concrete beams more accurately than the current approach. The ANN provides additional insight on the parameters that are most significant in estimating concrete shear strength, which may lead to a better understanding of the mechanism of shear failure.

Author Biographies

William A Young, Ohio University

W

Gary R Weckman, Ohio University

Gary Weckman was a faculty member at Texas A&M University-Kingsville for six years before joining the Ohio University faculty in 2002 as an associate professor in Industrial and Systems Engineering. Dr. Weckman’s primary research focus has been multidisciplinary applications utilizing knowledge extraction techniques with artificial neural networks. He has used ANNs to model complex systems such as large scale telecommunication network reliability, ecological relationships, stock market behavior, and industrial process scheduling. In addition, his research includes industrial safety and health applications and is on the Advisory Board for the University of Cincinnati NIOSH Occupational Safety and Health Education and Research Center Pilot Research Project.

Michael D Brown, Ohio University

Michael Brown graduated with a Ph.D. degree from The University of Texas at Austin in 2005. The research for his dissertation topic focused on the shear strength of reinforced concrete members and strut-and-tie modeling concrete structures. Dr. Brown received a Master’s degree (MSE) and Bachelor’s degree (BSCE) at The University of Texas at Austin in 2002 and 2000 respectively. The research performed during the completion of his Master's degree examined restrained shrinkage cracking in concrete bridge decks and the effects of concrete mixtures on such cracking. Dr. Brown is an associate member of ACI-ASCE Committee 445 Shear and Torsion.

Jim Thompson, Ohio University

Jim Thompson joined Ohio University's Civil Engineering Department in September 2002. He earned a Ph.D. in Civil Engineering at Lehigh University (2004) where his doctoral work focused on precast, pre-stressed concrete inverted tee girders. Prior to coming to OU, he worked as a Visiting Research Scientist at Lehigh University, testing composite ship hull sections. After earning his Master of Science in Engineering at The Johns Hopkins University (1992), Dr. Thompson spent approximately four years working as a structural engineer in Baltimore, MD, designing steel, masonry, and wood buildings. Dr. Thompson spent four years in the U. S. Navy's Civil Engineer Corps after earning his Bachelor of Mechanical Engineering degree at Villanova University (1985). While in the Navy, he served as the Public Works Officer for the Naval Facility in Adak, Alaska and the Officer-in-Charge of CBU-420 in Mayport, Florida.

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Published

2022-02-24

How to Cite

Young, W. A., Weckman, G. R., Brown, M. D., & Thompson, J. (2022). Extracting Knowledge of Concrete Shear Strength from Artificial Neural Networks. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(1), 26–35. https://doi.org/10.23055/ijietap.2008.15.1.59

Issue

Vol. 15 No. 1 (2008)

Section

Data Sciences and Computational Intelligence

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