Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction
Article
Article Title | Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction |
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ERA Journal ID | 211776 |
Article Category | Article |
Authors | Sharafati, Ahmad, Haghbin, Masoud, Aldlemy, Mohammed Suleman, Mussa, Mohamed H., Al Zand, Ahmed W., Ali, Mumtaz, Bhagat, Suraj Kumar, Al-Ansari, Nadhir and Yaseen, Zaher Mundher |
Journal Title | Applied Sciences |
Journal Citation | 10 (11), pp. 1-25 |
Article Number | 3811 |
Number of Pages | 25 |
Year | Jun 2020 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2076-3417 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/app10113811 |
Web Address (URL) | https://www.mdpi.com/2076-3417/10/11/3811 |
Abstract | High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters. |
Keywords | structure monitoring; shear strength prediction; machine learning; hybrid ANFIS model; high-strength concrete |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Byline Affiliations | Duy Tan University, Vietnam |
Islamic Azad University, Iran | |
College of Mechanical Engineering Technology, Libya | |
National University of Malaysia | |
University of Warith Al-Anbiyaa, Iraq | |
Deakin University | |
Ton Duc Thang University, Vietnam | |
Lulea University of Technology, Sweden |
https://research.usq.edu.au/item/w3065/development-of-advanced-computer-aid-model-for-shear-strength-of-concrete-slender-beam-prediction
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