New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns
Abstract
:1. Introduction
2. Data Provision
3. Methodology
3.1. ANFIS
3.2. Metaheuristic Algorithms
4. Results and Discussion
4.1. Metaheuristic Optimization
4.2. Prediction Results
4.3. Comparison
5. Conclusions
- Metaheuristic algorithms are suitable options for training neuro-fuzzy systems for the mentioned purpose.
- Referring to the correlation values >0.96, all employed fuzzy-metaheuristic models are capable of both comprehending and generalizing the relationship between the CC and input parameters.
- The PSO algorithm emerged as the most suitable optimizer for the ANFIS. This deduction came up due to the highest accuracy, as well as the most time-efficient optimization behavior observed compared to the three other algorithms.
- The PSO-ANFIS could present a finer prediction of extremum CC values.
- In short, the use of the PSO-ANFIS is recommended for practical applications which pursue efficient cost-competitive design of CCFST columns.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
CCFST | Circular concrete-filled steel tube | CC | Compression capacity |
ANN | Artificial neural network | ANFIS | Adaptive neuro-fuzzy inference system |
BART | Bayesian additive regression tree | GA | Genetic algorithm |
ABC | Artificial bee colony | PSO | Particle swarm optimization |
EWA | Earthworm algorithm | SSA | Salp swarm algorithm |
TLBO | Teaching learning-based optimization | fc’ | Compressive strength of UHSC |
L | Length of column | D | Diameter |
t | Thickness | fy | Yield stress |
fu | Ultimate stress of the steel tube | MF | Membership function |
RMSE | Root mean square error | R | Pearson correlation index |
MAPE | Mean absolute percentage error | MAE | Mean absolute error |
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Indicator | Factor | ||||||
---|---|---|---|---|---|---|---|
L [mm] | D [mm] | t [mm] | fy [MPa] | fu [MPa] | fc’ [MPa] | CC [kN] | |
Mean | 2475.0 | 450.0 | 15.2 | 331.3 | 460.0 | 150.0 | 30,185.3 |
Std. Error | 47.4 | 4 | 0.2 | 3.1 | 2.5 | 1.2 | 538.3 |
Std. Deviation | 1313.1 | 111.9 | 6.1 | 86 | 70.4 | 34.2 | 14,918.5 |
Sample Variance | 1,724,120 | 12,516.3 | 37.3 | 7401.8 | 4956.5 | 1168.2 | 222,561,708.9 |
Minimum | 900.0 | 300.0 | 6.0 | 235.0 | 360.0 | 100.0 | 8016.3 |
Maximum | 4800.0 | 600.0 | 30.0 | 460.0 | 540.0 | 200.0 | 75,051.6 |
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Karimi Sharafshadeh, B.; Ketabdari, M.J.; Azarsina, F.; Amiri, M.; Nehdi, M.L. New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns. Buildings 2023, 13, 125. https://doi.org/10.3390/buildings13010125
Karimi Sharafshadeh B, Ketabdari MJ, Azarsina F, Amiri M, Nehdi ML. New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns. Buildings. 2023; 13(1):125. https://doi.org/10.3390/buildings13010125
Chicago/Turabian StyleKarimi Sharafshadeh, Bizhan, Mohammad Javad Ketabdari, Farhood Azarsina, Mohammad Amiri, and Moncef L. Nehdi. 2023. "New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns" Buildings 13, no. 1: 125. https://doi.org/10.3390/buildings13010125