Reliability Analysis of RC Code for Predicting Load-Carrying Capacity of RCC Walls Through ANN

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Over the past couple of decades, a significant rise in utilization of artificial neural network (ANN) in the field of civil engineering has been observed. ANNs have been proven to be very helpful for researchers working in concrete technology. Reinforced cement concrete (RCC) shear walls play an important role in the stability of high-rise reinforced concrete structures. Current study is focused on using ANN-based design technique as an alternative to conventional design codes and physical models to estimate the ultimate load carrying capacity of RCC shear walls. In this study, database of 95 RCC wall samples has been collected from previously published literature. Various critical parameters considered for current research are; length of web portion of the wall (Lw), thickness of wall boundary member (bw), effective depth of wall (d), height of wall (H), shear span ratio (av/d), vertical steel ratio (ρv), horizontal steel ratio (ρh), yield strength of vertical and horizontal steel (fy), compressive strength of concrete (fc), and the ultimate load carrying capacity (Vexp).
Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning Techniques for Civil Engineering
EditorsVagelis Plevris, Afaq Ahmad, Nikos D. Lagaros
PublisherIGI Global
Number of pages31
ISBN (Electronic)9781668456446
ISBN (Print)9781668456439
Publication statusPublished - 2023

Publication series

NameAdvances in Civil and Industrial Engineering
ISSN (Print)2326-6139


Dive into the research topics of 'Reliability Analysis of RC Code for Predicting Load-Carrying Capacity of RCC Walls Through ANN'. Together they form a unique fingerprint.

Cite this