Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models
Abstract
:1. Introduction
2. Literature Review
2.1. Knowledge Gap
2.1.1. High-Strength Concrete
2.1.2. Structure Analysis
2.1.3. Sustainable Development Analysis
3. Research Methods
3.1. Equations Proposed in Published Literature
3.1.1. Formula Proposed by Khuntia et al.
3.1.2. Formula Proposed by Al-Ta’an et al.
3.1.3. Formula Proposed by Ashour et al.
3.1.4. Formula Proposed by Kara
3.2. Machine Learning Models
3.2.1. ANN Model
3.2.2. Xgboost Model
3.2.3. BNN Model
3.3. Data Preparation
3.4. Sustainable Development Analysis
4. Research Analysis
4.1. Predicting the Shear Strength
4.2. Predicting the Flexural Capacity
4.3. Predicting the Shear Stiffness
4.4. ML Models versus Proposed Formulas
5. Sensitivity Analysis with Shapley Additive Explanations (SHAP)
6. Sustainability Assessments
6.1. Greenhouse Gas Emission and Cost Budgets
6.2. Results Analysis
6.3. Sensitivity Analysis
7. Discussion of Findings
7.1. Structural Design
7.2. Sustainable Design
7.3. Future Scope
8. Conclusions
- A strong correlation coefficient R2 ≥ 0.89 is observed for the training, testing, and validating datasets of three ML models (ANN, Xgboost, and BNN). The comparison analysis illustrates that the BNN model performs better than the other ML models, with the highest predicted R2 of flexural capacity and shear stiffness, and the second higher predicted R2 of shear strength. The BNN model has been proven to have good prediction ability than traditional neural network.
- When comparing the error analysis of the models’ training phase, the Xgboost model has the lowest statistical errors and the highest R2, followed by the ANN and BNN models. However, the Xgboost model shows poorer performance in testing datasets, indicating its poor generalisation ability.
- In terms of the proposed empirical equations, the Ashour formula developed on the basis of regression analysis shows the best prediction ability with χ of 0.9579. However, the ML models proposed in this paper has the best shear strength prediction ability, where χ is 0.9716, 1.0044, 0.9579 for ANN, BNN and Xgboost models, respectively.
- In the section of sensitivity analysis, the longitudinal ratio and shear span show the strongest potential relationship with shear strength prediction. In contrast to the shear strength prediction, stiffness is sensitive to the synergetic effect among concrete, steel rebar, and fibre effect. In addition, the dimension effect exerts the greatest influence on the prediction of flexural capacity.
- This study proposed two models for the prediction of GHG emissions and cost budgets, which revealed that the fibre content has limited effect on the increase in GHG emissions and cost budgets. A strong correlation coefficient R2 ≥ 0.94 is observed for the training, testing, and validation datasets of three ML models (ANN, Xgboost, and CNN), and NN models outperform the other models. In this context, increasing the addition of fibre in the structure is a good way to balance both sustainable development and structure performance.
- Based on the proposed models in this study, the optimum design with the consideration of both structural and sustainable performance can be easily calculated. This study aimed to apply the ML models to real-world application. To achieve this, two studies were conducted, which revealed that the proposed ML models can be used to replace some function of FEM and evaluate the sustainable performance of concrete mixes. With the aid of the proposed models, it will be beneficial for researchers to improve design efficiency and support the strategy of sustainable development.
- The proposed models mainly focus on the structural performance and sustainable ability. As described in the section of discussion, the study did not address the resistance of corrosion resistance, fatigue, and the freeze-thaw cycle. Further works should focus on the durability and fatigue properties of high-strength fibre-reinforced concrete beams and expand the current datasets.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Author | Number of Beam | Compressive Strength MPa | Fibre Content % | Longitudinal Ratio % | Shear Span | Cross Section (W × D) mm | Fibre Type |
---|---|---|---|---|---|---|---|
Ashour, Hasanain and Wafa [15] | 18 | 92~101.32 | 0.5~1.5 | 0.37~4.58 | 1, 2, 4, 6 | 125 × 215 | Hooked |
Yoo and Yang [64] | 3 | 62.3 | 0.75 | 1.5 | 2, 4, 6 | 300 × 420 450 × 648 600 × 887 | Hooked |
Manju, et al. [65] | 6 | 82~83.8 | 0.5~1.5 | 1 | 1.5, 2.5 | 185 × 220 | Hooked |
Tahenni, et al. [66] | 16 | 63.1~65 | 0~3 | 1.16~1.5 | 2.2 | 100 × 135 | Hooked |
de Lima Araújo, et al. [67] | 1 | 58.87 | 1 | 1 | 1.5 | 370 × 350 | Hooked |
Kwak, et al. [68] | 9 | 62.6~68.6 | 0~0.75 | 1.5 | 2, 3, 4 | 125 × 212 | Hooked |
Alzahrani [69] | 6 | 61.6~73 | 0~0.75 | 1.46 | 3 | 200 × 350 | Hooked |
Singh and Jain [70] | 9 | 53.4~64.6 | 0.75~1.5 | 2.67 | 3.49 | 150 × 253 | Hooked |
Vamdewalle and Mortelmans [71] | 16 | 108.5~112 | 0~0.75 | 1.87 | 1.75, 2.5, 3.5, 4.5 | 200 × 300 | Hooked |
Cho and Kim [72] | 14 | 54.3~89.9 | 0~2 | 1.3~2.9 | 1.05 | 120 × 167.5 | Hooked |
Narayanan and Darwish [73] | 20 | 57.3~65.8 | 0.25~3 | 2~5.72 | 2, 2.5, 3 | 130 × 130 | Crimped |
Shin, et al. [74] | 13 | 80 | 0~1 | 3.59 | 2, 3, 4.5, 6 | 100 × 175 | Plain |
Noghabai [75] | 17 | 72~93.3 | 0.5~1 | 2.87~4.47 | 2.77~3.33 | 200 × 180 200 × 235 200 × 410 300 × 570 | Plain and Hooked |
Uomoto, et al. [76] | 4 | 54 | 1.5 | 2.2 | 1.5~2.5 | 182 × 182 | Plain |
Hwang, et al. [77] | 3 | 58~88 | 0.5~1 | 4.78 | 3 | 100 × 165 | Hooked |
Li, et al. [78] | 11 | 62.6 | 1 | 1.1~3.3 | 1~3 | 63.5 × 102 | Crimped |
Adebar, et al. [79] | 2 | 54.1~54.8 | 0.4~0.75 | 2.14 | 1.63 | 150 × 560 | Hooked |
Cohen and Aoude [80] | 1 | 59.4 | 0.5 | 1.52 | 3.77 | 125 × 212 | Hooked |
Pansuk, et al. [81] | 2 | 109.2~110.9 | 0.75 | 3.48 | 2.75 | 200 × 273 | Hooked |
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Scholar | ML Methodology | Best Predicting Ability | Beam Properties for Prediction |
---|---|---|---|
Research related to Steel Fibre-reinforced Concrete Beams | |||
Qian, Sufian, Hakamy, Farouk Deifalla and El-said [24] | SVR, MLP, Gradient boosting | R2 of 0.91 with Testing Datasets with Gradient boosting model | Flexural strength prediction of ultra-high-performance concrete |
Pakzad, et al. [28] | MLR, KNN, SVR, RF, GB, Xgboost, AdaBoost, ANN, and CNN. | R2 of 0.928 with Total Datasets with CNN model | Compressive strength prediction |
Kang, Yoo and Gupta [25] | MLR, KNN, SVR, RF, GB, Xgboost. | RMSE of 3.6144 with Total Datasets with Xgboost model | Compressive and flexural strength prediction |
Research related to Steel Fibre-reinforced Concrete Beams with Shear Strength Prediction | Number of Datasets | ||
Alzabeebee, et al. [29] | Evolutionary polynomial regression analysis | R2 of 0.93 with Testing and Training Datasets | 235 |
Jesika Rahman [27] | AdaBoost, CatBoost, Xgboost, ANN, SVR, et al. | R2 of 0.739 with Testing Datasets with Xgboost model | 507 |
A Shatnawi [26] | Gradient boosting regression tree | R2 of 0.969 with Training Datasets | 330 |
Shahnewaz and Alam [30] | Genetic Algorithm | R2 of 0.9 with total datasets | 358 |
Kara [31] | Genetic Programming | AAE of 11.39 | 101 |
Adhikary and Mutsuyoshi [32] | Neural Networks | SEM of 0.33 | 85 |
Yaseen [33] | M5, RF, and ELM | R2 of 0.87 with Testing Datasets with ELM model | 112 |
Authors | Formulas |
---|---|
Khuntia et al. [13] | |
Al-Ta’an et al. [14] | |
Ashour et al. [15] | for a/d>2.5 for a/d<2.5 |
Kara I F [31] |
Parameters | Title 2 | Title 3 |
---|---|---|
Compressive Strength MPa | 53.4 | 112 |
Fibre Content % | 0 | 3 |
Longitudinal Ratio % | 0.37 | 4.78 |
Shear Span | 1 | 3.77 |
Cross Section (W × D) mm | 100 × 135 | 600 × 887 |
Fibre Type | Hooked, Crimped and Plain |
Results | Train | Test | Total | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Models | R2 | MSE | MAE | R2 | MSE | MAE | R2 | MSE | MAE | |
Xgboost | 0.996 | 0.003 | 0.035 | 0.852 | 0.024 | 0.062 | 0.858 | 0.111 | 0.112 | |
ANN | 0.980 | 0.015 | 0.026 | 0.894 | 0.052 | 0.095 | 0.927 | 0.059 | 0.070 | |
BNN | 0.994 | 0.005 | 0.046 | 0.895 | 0.059 | 0.097 | 0.968 | 0.029 | 0.074 |
Results | Train | Test | Total | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Models | R2 | MSE | MAE | R2 | MSE | MAE | R2 | MSE | MAE | |
Xgboost | 0.996 | 0.003 | 0.035 | 0.852 | 0.024 | 0.062 | 0.858 | 0.111 | 0.112 | |
ANN | 0.980 | 0.015 | 0.026 | 0.894 | 0.052 | 0.095 | 0.927 | 0.059 | 0.070 | |
BNN | 0.994 | 0.005 | 0.046 | 0.895 | 0.059 | 0.097 | 0.968 | 0.029 | 0.074 |
Results | Train | Test | Total | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Models | R2 | MSE | MAE | R2 | MSE | MAE | R2 | MSE | MAE | |
Xgboost | 0.989 | 0.010 | 0.072 | 0.821 | 0.131 | 0.197 | 0.714 | 0.288 | 0.352 | |
ANN | 0.985 | 0.015 | 0.097 | 0.765 | 0.440 | 0.519 | 0.869 | 0.161 | 0.228 | |
BNN | 0.949 | 0.046 | 0.155 | 0.881 | 0.080 | 0.215 | 0.907 | 0.070 | 0.204 |
Kara | Al-Ta’an | Ashour | Khuntia | ANN | Xgboost | BNN | |
---|---|---|---|---|---|---|---|
SD | 0.449 | 0.368 | 0.366 | 0.457 | 0.113 | 0.139 | 0.187 |
Mean | 1.181 | 0.876 | 0.879 | 0.697 | 1.011 | 0.973 | 1.025 |
CV | 38.037 | 42.016 | 41.602 | 65.584 | 11.141 | 14.333 | 18.28 |
AAE | 0.375 | 0.238 | 0.274 | 0.434 | 0.151 | 0.223 | 0.103 |
χ | 0.9299 | 0.8403 | 0.9579 | 1.5153 | 0.9716 | 1.0044 | 0.9579 |
Constituents of Concrete | GWP/kg CO2 eq | Price/$/kg | Resource |
---|---|---|---|
Basic Concrete Composition | |||
Ordinary Portland Cement | 0.884 | 0.125 | Anderson and Moncaster [52] |
Coarse Aggregates | 0.00429 | 0.0099 | Ouellet-Plamondon and Habert [53] |
Fine Aggregates, Sand | 0.0024 | 0.0099 | Ouellet-Plamondon and Habert [53] |
Water | 0.00015 | 0.0016 | Ouellet-Plamondon and Habert [53] |
Supplementary Materials | |||
Silica Fume | 0.00313 | 0.5 | Ouellet-Plamondon and Habert [53] |
Blast Furnace Slag | 0.0329 | 0.05 | Kim, et al. [54] |
Superplasticizer | 0.749 | 25 | Ouellet-Plamondon and Habert [53] |
Steel Fibre | 2.2 | 1 | Qin and Kaewunruen [55] |
Steel Rebar | 0.72 | 0.56536 | Özdemir, et al. [56] |
Results | Train | Test | Total | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Models | R2 | MSE | MAE | R2 | MSE | MAE | R2 | MSE | MAE | |
Xgboost | 0.999 | 0.001 | 0.020 | 0.894 | 0.023 | 0.050 | 0.970 | 0.025 | 0.067 | |
ANN | 0.980 | 0.011 | 0.030 | 0.971 | 0.005 | 0.056 | 0.954 | 0.035 | 0.070 | |
BNN | 0.986 | 0.002 | 0.042 | 0.981 | 0.017 | 0.053 | 0.974 | 0.022 | 0.063 |
Results | Train | Test | Total | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Models | R2 | MSE | MAE | R2 | MSE | MAE | R2 | MSE | MAE | |
Xgboost | 0.999 | 0.001 | 0.019 | 0.852 | 0.043 | 0.077 | 0.973 | 0.023 | 0.080 | |
ANN | 0.981 | 0.009 | 0.024 | 0.980 | 0.002 | 0.035 | 0.943 | 0.039 | 0.056 | |
BNN | 0.989 | 0.018 | 0.053 | 0.981 | 0.002 | 0.042 | 0.977 | 0.020 | 0.082 |
Concrete | Compressive Strength | Fibre Content | Aspect Ratio | Shear Force (kN) | Shear Stiffness (kN/mm) |
---|---|---|---|---|---|
HSC | 65 | 0 | - | 30.81 | 18.19 |
FRHSC-1-60 | 64 | 1 | 65 | 46.28 | 18.51 |
FRHSC-1-85 | 60 | 2 | 80 | 50.82 | 22.18 |
FRHSC-2-60 | 63.1 | 1 | 65 | 46.87 | 20.06 |
FRHSC-2-85 | 65 | 2 | 80 | 52.5 | 22.85 |
Beam Information | |||||
Cross Section | 100 × 150 mm | ||||
Shear Span Ratio | 2.2 | ||||
Longitudinal reinforcement ratio | 1.16% |
Results | Shear Strength (MPa) | Shear Stiffness (kN/mm) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Models | HSC | FRHSC-1-60 | FRHSC-1-85 | FRHSC-2-60 | FRHSC-2-85 | HSC | FRHSC-1-60 | FRHSC-1-85 | FRHSC-2-60 | FRHSC-2-85 | |
Exp | 2.33 | 3.51 | 3.85 | 3.55 | 3.98 | 18.19 | 18.51 | 22.18 | 20.06 | 22.85 | |
FEM | - | 3.34 | 3.83 | - | - | - | 18.11 | 20.85 | - | - | |
Exp/FEM | - | 1.05 | 1.00 | - | - | - | 1.02 | 1.06 | - | - | |
BNN | 2.93 | 3.30 | 3.64 | 3.63 | 4.02 | 17.11 | 18.62 | 24.37 | 19.37 | 29.44 | |
Exp/BNN | 0.80 | 1.06 | 1.06 | 0.98 | 0.99 | 1.06 | 0.99 | 0.91 | 1.04 | 0.78 |
Mix From | Cement | Water | Sand | Gravel | Fibre Content | SP | Slag | SF |
---|---|---|---|---|---|---|---|---|
Gao, et al. [61] | 529 | 164 | 646 | 1110 | 0, 0.5%, 1% and 1.5% | 6.348 | - | - |
Zheng, et al. [62] | 451.8 | 164 | 660.8 | 1078.2 | 0, 0.5%, 1%, 1.5% and 2% | 4.9 | - | - |
Li, et al. [63] | 400 | 164.3 | 557.2 | 1099.5 | 0, 0.5%, 1% and 1.5% | 7.2 | 25 | 105 |
Fibre Content | Shear (MPa) | Flexural (N·m) | Stiffness (N/mm) 103 | Carbon Emission (kg CO2 eq) | Cost ($) | Ranking | |
---|---|---|---|---|---|---|---|
Gao, Huang, Yuan and Gu [61] | 0 | 1.46 | 863.98 | 34.16 | 237.96 | 111.85 | 3 |
0.5 | 1.91 | 964.11 | 35.21 | 240.53 | 113.69 | ||
1 | 2.38 | 1070.57 | 39.55 | 243.06 | 115.51 | ||
1.5 | 3.04 | 1163.95 | 42.24 | 245.56 | 117.32 | ||
Zheng, Wu, He, Shang, Xu and Sun [62] | 0 | 1.25 | 744.42 | 27.05 | 220.13 | 100.33 | 2 |
0.5 | 1.73 | 832.17 | 28.77 | 222.68 | 102.16 | ||
1 | 2.21 | 1038.90 | 31.86 | 225.20 | 104.00 | ||
1.5 | 2.75 | 1108.79 | 35.25 | 227.70 | 105.84 | ||
2 | 3.49 | 1112.13 | 38.64 | 230.20 | 107.70 | ||
Li, Xue, Fu, Yao and Liu [63] | 0 | 1.44 | 897.17 | 34.12 | 226.82 | 89.25 | 1 |
0.5 | 1.89 | 1051.47 | 35.20 | 229.34 | 91.12 | ||
1 | 2.36 | 1088.22 | 39.56 | 231.84 | 92.99 | ||
1.5 | 2.99 | 1130.04 | 41.66 | 234.34 | 94.85 |
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Qin, X.; Kaewunruen, S. Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models. Sustainability 2023, 15, 6640. https://doi.org/10.3390/su15086640
Qin X, Kaewunruen S. Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models. Sustainability. 2023; 15(8):6640. https://doi.org/10.3390/su15086640
Chicago/Turabian StyleQin, Xia, and Sakdirat Kaewunruen. 2023. "Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models" Sustainability 15, no. 8: 6640. https://doi.org/10.3390/su15086640