Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning
Abstract
1. Introduction
2. Methodology
2.1. Specimens Design
2.2. Material Properties
2.2.1. Square Steel Tube
2.2.2. Fiber Material
2.2.3. Aggregate Gradation and Physical Properties
2.2.4. Concrete Mix
2.3. Sample Preparation
2.4. Test Scheme and Instrument
3. Test Results and Discussions
3.1. Experimental Phenomena and Damage Patterns
3.2. Load-Displacement Behavior
3.3. Load-Strain Curve
3.4. Mechanical Property Evaluation Index
3.4.1. Strength Indicators
3.4.2. Ductility Coefficient
4. Finite Element Analysis
4.1. Ontological Relationships and Finite Element Modeling
4.2. Comparative Analysis of Experimental and Simulation Results
4.3. Fiber Mixing Effect Coefficient
4.3.1. Influence of Fiber Mixing Effects on Ultimate Load Carrying Capacity
4.3.2. Fiber Mixing Effect Coefficient of Ductility
4.4. SEM Image Analysis
4.5. Ultimate Bearing Capacity Analysis
5. Machine Learning Modeling
5.1. Database Construction and Parameter Analysis
5.2. NRBO-XGBoost Modeling
5.2.1. XGBoost Algorithm
5.2.2. NRBO Algorithm
5.2.3. Application of NRBO-XGBoost to BSFCFST Ultimate Bearing Capacity Prediction
5.3. NRBO-XGBoost Model Performance Evaluation
5.3.1. Evaluation Metrics
5.3.2. Comparative Analysis with Existing Prediction Methods
6. Conclusions
- (1)
- A new type of combined column, square steel tube hybrid steel–basalt fiber reinforced concrete column (BSFCFST) is proposed in this paper.
- (2)
- The axial mechanical properties of BSFCFST were experimentally investigated and the effects of different hybrid fiber admixtures on the mechanical properties of steel tube concrete columns were quantitatively analyzed using the finite element method.
- (3)
- A new combined machine learning model NRBO-XGBoost is proposed based on the Newton–Raphson algorithm and limit gradient boosting model, which can be used for the prediction of the axial compressive load capacity of BSFCFST, and the prediction effect is good.
- (1)
- It can be seen from the test that the steel–basalt hybrid fibers have little effect on the damage mode of the steel tube concrete columns, but there is a certain increase in the ultimate bearing capacity of the specimens, with the increase in the content of steel fibers in the hybrid fibers; the ultimate bearing capacity of the specimens was increased by 16.0%, 30.0%, and 33.8%, respectively. The presence of mixed fibers played a certain role in delaying the damage process of steel tube concrete columns, and the ductility coefficient increased significantly with the increase in the content of mixed fibers, which was due to the random distribution of short-cut basalt fibers in the matrix to form a micro-reinforcement network, and steel fibers to provide macroscopic skeleton support, which formed a “micro-macro” composite reinforcement system to optimize the stress distribution. The two form a “micro-macro” composite reinforcement system, which optimizes the stress distribution.
- (2)
- Based on Abaqus modeling analysis, it can be concluded that the coefficient of mixed fiber effect is around 1.0 for ultimate bearing capacity, and the coefficient for ductility is generally greater than 1.0, reaching a maximum of 1.3544, which indicates that the fiber mixing effect of steel–basalt mixed fiber does not contribute significantly to the ultimate bearing capacity of the specimen, but the fiber mixing effect obviously enhances the steel tube concrete column’s ductility, and the enhancement effect is better than the sum of the effects of each single fiber.
- (3)
- Based on the study of the mechanical properties of BSFCFST in this paper, a new machine learning model, NRBO-XGBoost, is proposed using the NRBO algorithm and the XGBoost model. By comparing the computational results of the five classical machine learning models, and the three canonical formulas of each country, the NRBO-XGBoost model has an excellent prediction accuracy and a good generalization performance, with R2 = 0.988. It is also found that the NRBO algorithm improves the computational accuracy of the XGBoost model significantly, which is due to the fact that the Newton–Raphson Search Rule (NRSR) utilizes the information of the second-order derivatives of the objective function, which is highly compatible with the optimization logic based on the second-order Taylor expansion of XGBoost itself. Taken together, the NRBO-XGBoost model has high prediction accuracy for the BSFCFST, which is a good alternative to the existing prediction methods and can provide a theoretical reference for the engineering application of the BSFCFST.
7. Foresight
- (1)
- The axial mechanical properties of BSFCFST have been thoroughly investigated in this paper, and the degradation of BSFCFST under actual working conditions such as long-term loading, fatigue cycling, and corrosive environments will be further investigated in the future.
- (2)
- The actual engineering components are often in the compression, bending and shear composite stress state, the future will carry out tests and simulations under eccentric compression, torsion and impact loads, and develop an improved machine learning prediction framework applicable to multi-dimensional stress state.
- (3)
- The environmental and economic benefits of hybrid fibers have not yet been quantified, and future work will be carried out to optimize the fiber blending thresholds to balance performance enhancement and cost increase.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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fy/MPa | fu/MPa | ΔL/L (%) | ES/GPa | μ |
---|---|---|---|---|
331 | 411 | 28 | 206 | 0.3 |
Type | df/µm | lf/mm | ρ/(kg/m2) | ft/MPa | ES/GPa |
---|---|---|---|---|---|
Steel fiber | 200 | 12–14 | 7850 | 2850 | 206 |
Basalt fiber | 7–15 | 12 | 2640 | 3000–4800 | 91–110 |
Aggregate Type | Fineness Module | Mud Content/% | Packing Density/(kg·m3) | Apparent Density/(kg·m3) | Moisture Content/% | Maximum Particle Size/mm |
---|---|---|---|---|---|---|
medium sand | 2.58 | 1.6% | 1490 | 2580 | 1.2 | 4 |
gravel | \ | 0.9% | 1572 | 2655 | \ | 30 |
Water Reduction Rate | Slump Loss 1 h | Slump Loss 2 h | Gas Content | 28 d Shrinkage Rate | Water Secretion Rate |
---|---|---|---|---|---|
40% | ≤5% | ≤10% | 2–3% | ≤100% | ≤60% |
Group | Cement | Fly | Gravel | Sand | Water (kg⋅m3) | VB (%) | VS (%) | M (%) | D (mm) | fcu (N/mm2) | fcr (N/mm2) |
---|---|---|---|---|---|---|---|---|---|---|---|
B0-S0 | 390 | 170 | 670 | 820 | 230 | 0 | 0 | 2.00 | 618 | 40.7 | 37.5 |
B0.1-S0 | 390 | 170 | 670 | 820 | 230 | 0.1 | 0 | 2.02 | 597 | 44.9 | 37.7 |
B0.1-S0.6 | 390 | 170 | 670 | 820 | 230 | 0.1 | 0.6 | 2.04 | 566 | 49.0 | 39.6 |
B0.1-S1.2 | 390 | 170 | 670 | 820 | 230 | 0.1 | 1.2 | 2.05 | 527 | 50.9 | 42.1 |
B0.1-S1.6 | 390 | 170 | 670 | 820 | 230 | 0.1 | 1.6 | 2.07 | 502 | 51.0 | 43.9 |
Specimen ID | L × B × t (mm) | fy (N/mm2) | fcr (N/mm2) | λ | ξ | VB (%) | Vs (%) |
---|---|---|---|---|---|---|---|
L6-t2-B0S0 | 600 × 100 × 2 | 331 | 37.5 | 20.78 | 0.751 | 0 | 0 |
L6-t2-B0.1S0 | 600 × 100 × 2 | 331 | 37.7 | 20.78 | 0.747 | 0.1 | 0 |
L6-t2-B0.1S0.6 | 600 × 100 × 2 | 331 | 39.6 | 20.78 | 0.711 | 0.1 | 0.6 |
L6-t2-B0.1S1.2 | 600 × 100 × 2 | 331 | 42.1 | 20.78 | 0.669 | 0.1 | 1.2 |
L6-t2-B0.1S1.6 | 600 × 100 × 2 | 331 | 43.9 | 20.78 | 0.641 | 0.1 | 1.6 |
L6-t1.5-B0.1S1.2 | 600 × 100 × 1.5 | 331 | 42.1 | 20.78 | 0.494 | 0.1 | 1.2 |
L6-t2.5-B0.1S1.2 | 600 × 100 × 2.5 | 331 | 42.1 | 20.78 | 0.849 | 0.1 | 1.2 |
L6-t3-B0.1S1.2 | 600 × 100 × 3 | 331 | 42.1 | 20.78 | 1.036 | 0.1 | 1.2 |
L4-t2-B0.1S1.2 | 400 × 100 × 2 | 331 | 42.1 | 13.86 | 0.669 | 0.1 | 1.2 |
L8-t2-B0.1S1.2 | 800 × 100 × 2 | 331 | 42.1 | 27.71 | 0.669 | 0.1 | 1.2 |
L10-t2-B0.1S1.2 | 1000 × 100 × 2 | 331 | 42.1 | 34.64 | 0.669 | 0.1 | 1.2 |
Specimen ID | L × B × t | fy (N/mm2) | fCU (N/mm2) | VB (%) | Vs (%) | SI | DI | NU (KN) | |
---|---|---|---|---|---|---|---|---|---|
Data of this test | L6-t2-B0S0 | 600 × 100 × 2 | 331 | 40.7 | 0 | 0 | 1.09 | 1.80 | 554 |
L6-t2-B0.1S0 | 600 × 100 × 2 | 331 | 44.9 | 0.1 | 0 | 1.18 | 1.88 | 633 | |
L6-t2-B0.1S0.6 | 600 × 100 × 2 | 331 | 49.0 | 0.1 | 0.6 | 1.22 | 2.73 | 687 | |
L6-t2-B0.1S1.2 | 600 × 100 × 2 | 331 | 50.9 | 0.1 | 1.2 | 1.26 | 3.36 | 722 | |
L6-t2-B0.1S1.6 | 600 × 100 × 2 | 331 | 51.0 | 0.1 | 1.6 | 1.29 | 3.45 | 741 | |
L6-t1.5-B0.1S1.2 | 600 × 100 × 1.5 | 331 | 50.9 | 0.1 | 1.2 | 1.24 | 3.21 | 638 | |
L6-t2.5-B0.1S1.2 | 600 × 100 × 2.5 | 331 | 50.9 | 0.1 | 1.2 | 1.27 | 3.46 | 797 | |
L6-t3-B0.1S1.2 | 600 × 100 × 3 | 331 | 50.9 | 0.1 | 1.2 | 1.28 | 3.55 | 868 | |
L4-t2-B0.1S1.2 | 400 × 100 × 2 | 331 | 50.9 | 0.1 | 1.2 | 1.29 | 3.42 | 739 | |
L8-t2-B0.1S1.2 | 800 × 100 × 2 | 331 | 50.9 | 0.1 | 1.2 | 1.22 | 2.68 | 700 | |
L10-t2-B0.1S1.2 | 1000 × 100 × 2 | 331 | 50.9 | 0.1 | 1.2 | 1.18 | 2.12 | 674 | |
Data of Document [45] | L6-t2-S0 | 600 × 100 × 2 | 331 | 46.6 | 0 | 0 | 1.12 | 1.83 | 617 |
L6-t2-S0.6 | 600 × 100 × 2 | 331 | 47.0 | 0 | 0.6 | 1.20 | 2.08 | 660 | |
L6-t2-S0.9 | 600 × 100 × 2 | 331 | 49.0 | 0 | 0.9 | 1.21 | 2.62 | 682 | |
L6-t2-S1.2 | 600 × 100 × 2 | 331 | 51.5 | 0 | 1.2 | 1.21 | 3.12 | 700 |
αe | αNu of Ultimate Capacity | αDI of Ductility | |||||||
---|---|---|---|---|---|---|---|---|---|
BF (%) | BF (%) | ||||||||
0.02 | 0.04 | 0.06 | 0.10 | 0.02 | 0.04 | 0.06 | 0.10 | ||
SF (%) | 0.6 | 0.9972 | 1.0008 | 1.0063 | 1.0146 | 1.3523 | 1.3544 | 1.3217 | 1.3125 |
negative | positive | positive | positive | positive | positive | positive | positive | ||
0.9 | 0.9885 | 0.9988 | 1.0032 | 1.0104 | 1.3012 | 1.2684 | 1.2213 | 1.2126 | |
negative | negative | positive | positive | positive | positive | positive | positive | ||
1.2 | 0.9822 | 0.9972 | 1.0012 | 1.0053 | 1.2565 | 1.2172 | 1.1554 | 1.0769 | |
negative | negative | positive | positive | positive | positive | positive | positive | ||
1.6 | 0.9524 | 0.9803 | 1.0004 | 1.0022 | 1.1056 | 1.0723 | 1.0055 | 0.9982 | |
negative | negative | positive | positive | positive | positive | positive | negative |
Specimen ID | Nu (kN) | EC-4 | AIJ | GB 50936-2014 | FE | ||||
---|---|---|---|---|---|---|---|---|---|
N | N/Nu | N | N/Nu | N | N/Nu | N | N/Nu | ||
L6-t2-B0S0 | 554 | 489.9 | 0.88 | 553.3 | 1.00 | 619.5 | 1.12 | 552.0 | 1.01 |
L6-t2-B0.1S0 | 633 | 490.5 | 0.77 | 554.8 | 0.88 | 623.1 | 0.95 | 630.1 | 1.01 |
L6-t2-B0.1S0.6 | 687 | 501.5 | 0.73 | 569.7 | 0.83 | 658.3 | 0.90 | 679.8 | 0.99 |
L6-t2-B0.1S1.2 | 722 | 516.8 | 0.72 | 589.3 | 0.82 | 699.8 | 0.91 | 719.2 | 1.01 |
L6-t2-B0.1S1.6 | 741 | 527.8 | 0.71 | 603.4 | 0.81 | 727.4 | 0.94 | 745.0 | 1.03 |
L6-t1.5-B0.1S1.2 | 638 | 452.5 | 0.71 | 532.3 | 0.83 | 541.2 | 0.85 | 632.5 | 0.97 |
L6-t2.5-B0.1S1.2 | 797 | 579.8 | 0.73 | 645.7 | 0.81 | 832.6 | 0.98 | 800.9 | 1.02 |
L6-t3-B0.1S1.2 | 868 | 643.2 | 0.74 | 701.5 | 0.81 | 901.2 | 0.97 | 871.7 | 1.01 |
L4-t2-B0.1S1.2 | 739 | 535.6 | 0.72 | 598.3 | 0.81 | 720.4 | 0.95 | 735.0 | 0.99 |
L8-t2-B0.1S1.2 | 700 | 500.2 | 0.71 | 586.7 | 0.84 | 681.2 | 0.93 | 705.8 | 1.03 |
L10-t2-B0.1S1.2 | 674 | 488.7 | 0.73 | 572.8 | 0.85 | 642.8 | 0.94 | 678.2 | 1.01 |
Mean value | 0.74 | 0.84 | 0.94 | 0.99 | |||||
Standard deviation | 0.050 | 0.056 | 0.066 | 0.006 |
Parameter | Unit | Avg | Max | Min | Std | Type |
---|---|---|---|---|---|---|
L | mm | 1050.9 | 3000.0 | 400.0 | 742.9 | Input |
B | mm | 171.4 | 250.0 | 100.0 | 51.4 | Input |
t | mm | 3.3 | 5.0 | 1.5 | 1.0 | Input |
fcu | Mpa | 42.1 | 55.0 | 30.0 | 8.6 | Input |
fy | Mpa | 347.3 | 500.0 | 235.0 | 69.6 | Input |
VS | % | 1.10 | 2.00 | 0.2 | 0.57 | Input |
VB | % | 0.06 | 0.10 | 0.02 | 0.03 | Input |
Nu | Mpa | 1659.9 | 3890.2 | 412.7 | 847.6 | Output |
Hyperparameters | Search Range | Default Value | NRBO-XGBoost |
---|---|---|---|
n_estimators | (50, 500) | 100 | 437 |
learning_rate | (0.01, 0.6) | 0.3 | 0.2564 |
max_depth | (3, 10) | 6 | 3 |
min_child_weight | (1, 3) | 1 | 1.2565 |
gamma | (0, 1) | 0 | 0.4736 |
subsample | (0.5, 1) | 1 | 0.5907 |
colsample_bytree | (0.5, 1) | 1 | 1 |
Type | Model | R2 | RMSE | MAPE/% | T/h |
---|---|---|---|---|---|
The test | NRBO-XGBoost | 0.988 | 27.826 | 1.06 | 0.58 |
Gauge formula | EC 4 | 0.654 | 371.596 | 28.07 | / |
AIJ | 0.712 | 318.428 | 10.89 | / | |
GB 50936 | 0.822 | 126.375 | 6.76 | / | |
Classical machine learning model | BPNN | 0.944 | 243.868 | 10.58 | 0.52 |
SVM | 0.551 | 688.889 | 30.41 | 2.50 | |
RF | 0.868 | 374.251 | 16.29 | 1.22 | |
AdaBoost | 0.803 | 456.418 | 25.05 | 0.70 | |
XGBoost | 0.920 | 290.920 | 12.47 | 0.63 |
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Zhang, B.; Xu, X.; Hao, W. Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning. Buildings 2025, 15, 1859. https://doi.org/10.3390/buildings15111859
Zhang B, Xu X, Hao W. Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning. Buildings. 2025; 15(11):1859. https://doi.org/10.3390/buildings15111859
Chicago/Turabian StyleZhang, Bohao, Xiao Xu, and Wenxiu Hao. 2025. "Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning" Buildings 15, no. 11: 1859. https://doi.org/10.3390/buildings15111859
APA StyleZhang, B., Xu, X., & Hao, W. (2025). Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning. Buildings, 15(11), 1859. https://doi.org/10.3390/buildings15111859