Journal ArticleDOI
Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model
Guangnan Zhang,Zainab Hasan Ali,Mohammed Suleman Aldlemy,Mohamed H. Mussa,Sinan Q. Salih,Sinan Q. Salih,Mohammed Majeed Hameed,Zainab S. Al-Khafaji,Zaher Mundher Yaseen +8 more
TLDR
The proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.Abstract:
The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.read more
Citations
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Journal ArticleDOI
Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment
TL;DR: In this article, a new strategy for finding tensile strength retention (TSR) using empirical models based on the strong non-linear ability of artificial intelligence techniques, i.e., artificial neuro-networking (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), was presented.
Journal ArticleDOI
Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model
Hai Tao,Ahmed A. Ewees,Ahmed A. Ewees,Ali Omran Al-Sulttani,Ufuk Beyaztas,Mohammed Majeed Hameed,Sinan Q. Salih,Sinan Q. Salih,Asaad M. Armanuos,Nadhir Al-Ansari,Cyril Voyant,Shamsuddin Shahid,Zaher Mundher Yaseen +12 more
TL;DR: In this article, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm(GOA), was proposed for global solar radiation (SR) prediction at different locations of North Dakota, USA.
Journal ArticleDOI
Machine learning-based constitutive models for cement-grouted coal specimens under shearing
TL;DR: In this paper, a constitutive law of CGCS was developed using hybrid machine learning (ML) algorithms, and regression tree (RT) was used as the main technique to maintain the interpretation of the trained ML models.
Journal ArticleDOI
Predicting load capacity of shear walls using SVR–RSM model
TL;DR: A novel hybrid intelligent model to predict the ultimate shear capacity of RCSW is proposed based on two calibrating strategies in a novel hybrid modeling approach called RSM–SVR, demonstrating robust tendency and much lower uncertainty.
Journal ArticleDOI
Prediction of high-strength concrete: high-order response surface methodology modeling approach
TL;DR: High-order response surface methodology (HORSM) is used to develop a prediction model to accurately predict the compressive strength of high-strength concrete (HSC) and it has great potential in the field of concrete technology.
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