Neural Network-Based Prediction: The Case of Reinforced Concrete Members under Simple and Complex Loading

Applied Sciences

Afaq Ahmad¹  NikosD. Lagaros²  DemetriosM. Cotsovos³ 

[1] Department of Civil Engineering, University of Engineering & Technology Taxila, Taxila 47050, Pakistan;Department of Structural Engineering, Institute of Structural Analysis & Antiseismic Research, School of Civil Engineering, National Technical University of Athens, 9, Heroon Polytechniou Str., Zografou Campus, GR-15780 Athens, Greece;Structural Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK;

关键词: RC beam;    T-beam;    columns;    slab;    ACI;    EC2;   

DOI  :  10.3390/app11114975

来源: DOAJ

【 摘 要 】

The objective of this study is to compare conventional models used for estimating the load carrying capacity of reinforced concrete (RC) members, i.e., Current Design Codes (CDCs), with the method based on different assumptions, i.e., the Compressive Force Path (CFP) method and a non-conventional problem solver, i.e., an Artificial Neural Network (ANN). For this purpose, four different databases with the details of the critical parameters of (i) RC beams in simply supported conditions without transverse steel or stirrups (BWOS) and RC beams in simply supported conditions with transverse steel or stirrups (BWS), (ii) RC columns with cantilever-supported conditions (CWA), (iii) RC T-beams in simply supported conditions without transverse steel or stirrups (TBWOS) and RC T-beams in simply supported conditions with transverse steel or stirrups (TBWS) and (iv) RC flat slabs in simply supported conditions under a punching load (SCS) are developed based on the data from available experimental studies. These databases obtained from the published experimental studies helped us to estimate the member response at the ultimate limit-state (ULS). The results show that the predictions of the CFP and the ANNs often correlate closer to the experimental data as compared to the CDCs.

【 授权许可】

Unknown