TY - CHAP
T1 - Reliability Analysis of RC Code for Predicting Load-Carrying Capacity of RCC Walls Through ANN
AU - Ahmad, Afaq
AU - Cotsovos, Demitrios M.
PY - 2023
Y1 - 2023
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85167687952&partnerID=8YFLogxK
U2 - 10.4018/978-1-6684-5643-9.ch009
DO - 10.4018/978-1-6684-5643-9.ch009
M3 - Chapter (peer-reviewed)
SN - 9781668456439
T3 - Advances in Civil and Industrial Engineering
SP - 216
EP - 246
BT - Artificial Intelligence and Machine Learning Techniques for Civil Engineering
A2 - Plevris, Vagelis
A2 - Ahmad, Afaq
A2 - Lagaros, Nikos D.
PB - IGI Global
ER -