Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups
Abstract
:1. Introduction
1.1. ACI 318-19
1.2. The Gandomi Model
1.3. Gene Expression (GEP) Model
2. Description of Experimental Database
3. Gene Expression Programming
4. Results and Discussion
4.1. Assessment of the Significance of the Features
4.2. Evaluation of the Prediction Accuracy
4.3. Results of Model Prediction
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Number | Symbols | Definition |
---|---|---|
1 | distribution of beam width | |
2 | effective beam depth | |
3 | concrete compressive strength | |
4 | shear span ratio | |
5 | longitudinal steel-structured proportionality | |
6 | longitudinal steel yield stress | |
7 | estimated shear strength of ACI318-19 | |
8 | the factor that considers the type of concrete | |
9 | the impact of the size factor | |
10 | estimated shear strength from Equation (2) | |
11 | the maximum size of coarse aggregate | |
12 | the proposed shear strength | |
13 | root mean square error | |
14 | mean absolute error | |
15 | mean absolute percentage error | |
16 | R2 | coefficient of determination |
17 | real quantities of the shear strength | |
18 | average of | |
19 | total number of datasets used |
Features | Mean | Median | Mode | Standard Deviation | Minimum | Maximum | Q1 | Q2 | Q3 |
---|---|---|---|---|---|---|---|---|---|
218.6 | 153.1 | 152.4 | 207.1 | 50.0 | 3005.0 | 150 | 153 | 203.2 | |
3.5 | 3.2 | 3.0 | 1.0 | 2.4 | 8.1 | 2.9 | 3.2 | 4 | |
345.5 | 268.2 | 252.5 | 303.3 | 57.2 | 3000.0 | 203.6 | 286.2 | 342 | |
1.9 | 1.8 | 2.6 | 1.1 | 0.1 | 6.6 | 1 | 2 | 3 | |
449.9 | 421.0 | 414.0 | 153.6 | 174.0 | 1779.0 | 370 | 421 | 494 | |
37.4 | 30.4 | 49.4 | 20.0 | 12.3 | 132.1 | 24.9 | 30.4 | 41.3 | |
98.2 | 61.1 | 53.4 | 124.0 | 7.2 | 1308.4 | 41.1 | 61.1 | 106.7 |
Parameter | Setting |
---|---|
Number of samples | 784 |
Fitness criteria | RMSE |
Output parameter (shear strength) | 1 |
input parameters | 5 |
Number of genes | 3 |
Mathematical operations | |
Depth of tree | 8 |
The mathematical operation to link ETs | Multiplication |
Parameter to Shear Strength | Symbol | Symbol Explanation |
---|---|---|
Beam width, mm | ||
Effective beam depth, mm | ||
Shear span-to-effective depth ratio | ||
Flexural steel ratio, % | ||
Yield stress of flexural steel, MPa | ||
Concrete compressive strength, MPa |
Performance Metrics | Prediction Models | ||
---|---|---|---|
Equation (1) | Gene Expression | Equation (2) | |
28.5 | 21.5 | 27.98 | |
47.5 | 35.2 | 82.4 | |
30.8 | 29.4 | 23.9 | |
24.92 | 25.95 | 88.6 |
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Alshboul, O.; Almasabha, G.; Shehadeh, A.; Mamlook, R.E.A.; Almuflih, A.S.; Almakayeel, N. Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups. Buildings 2022, 12, 1166. https://doi.org/10.3390/buildings12081166
Alshboul O, Almasabha G, Shehadeh A, Mamlook REA, Almuflih AS, Almakayeel N. Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups. Buildings. 2022; 12(8):1166. https://doi.org/10.3390/buildings12081166
Chicago/Turabian StyleAlshboul, Odey, Ghassan Almasabha, Ali Shehadeh, Rabia Emhamed Al Mamlook, Ali Saeed Almuflih, and Naif Almakayeel. 2022. "Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups" Buildings 12, no. 8: 1166. https://doi.org/10.3390/buildings12081166