Journal ArticleDOI
Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength
Maryam Safa,Mahdi Shariati,Zainah Ibrahim,Ali Toghroli,Shahrizan Baharom,Norazman Mohamad Nor,Dalibor Petković +6 more
TLDR
In this paper, the shear strength of steel-concrete composite beams was analyzed based on the respective variable parameters and the methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) was adopted for this purpose.Abstract:
Structural design of a composite beam is influenced by two main factors, strength and ductility. For the design to be effective for a composite beam, say an RC slab and a steel I beam, the shear strength of the composite beam and ductility have to carefully estimate with the help of displacements between the two members. In this investigation the shear strengths of steel-concrete composite beams was analyzed based on the respective variable parameters. The methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) has been adopted for this purpose. The detection of the predominant factors affecting the shear strength steel-concrete composite beam was achieved by use of ANFIS process for variable selection. The results show that concrete compression strength has the highest influence on the shear strength capacity of composite beam.read more
Citations
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Journal ArticleDOI
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
TL;DR: The developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars and an ambitious attempt to reveal the nature of mortar materials has been made.
Journal ArticleDOI
A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement
Mahdi Shariati,Mohammad Saeed Mafipour,Behzad Ghahremani,Fazel Azarhomayun,Masoud Ahmadi,Nguyen Thoi Trung,Ali Shariati +6 more
TL;DR: The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model, and it is deducted that the ELm-GWO model is capable of reaching superior performance indices in comparison with those of the other models.
Journal ArticleDOI
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
Mahdi Shariati,Mohammad Saeed Mafipour,Peyman Mehrabi,Alireza Bahadori,Yousef Zandi,Musab N.A. Salih,Hoang Nguyen,Jie Dou,Xuan Song,Shek Poi-Ngian +9 more
TL;DR: Investigation of the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC) revealed that an ANN model could properly predict the behavior of channel connector and eliminate the need for conducting costly experiments to some extent.
Journal ArticleDOI
Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns
TL;DR: In this paper, several advanced methods were applied and developed to predict the bearing capacity of the concrete-filled steel tube (CFST) columns in two phases of prediction and optimization.
Journal ArticleDOI
A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques
Mahdi Shariati,Mohammad Saeed Mafipour,Peyman Mehrabi,Ali Shariati,Ali Toghroli,Nguyen Thoi Trung,Musab N.A. Salih +6 more
TL;DR: It is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction and it is demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.
References
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Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles
TL;DR: An ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) is described and the simulated test flights indicate the capability of the approach in achieving the desired performance.
Journal ArticleDOI
Prediction of building energy needs in early stage of design by using ANFIS
TL;DR: It is observed that ANFIS can be a strong tool with the 96.5 and 83.8% for heating and cooling energy prediction in pre-design stage of energy efficient buildings for choosing the best combinations.
Journal ArticleDOI
Prediction of shear capacity of channel shear connectors using the ANFIS model
Nor Hafizah Ramli Sulong,Ali Toghroli,Mohammad Mohammadhassani,Mahdi Shariati,Meldi Suhatril,Zainah Ibrahim +5 more
TL;DR: The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the classical linear regressions.
Journal ArticleDOI
Finite element modelling of composite beams with full and partial shear connection
TL;DR: In this paper, the authors focus on the evaluation of full and partial shear connection in composite beams using the commercial finite element (FE) software ANSYS, which is able to simulate the overall flexural behaviour of simply supported composite beams subjected to either concentrated or uniformly distributed loads.