Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete
Abstract
1. Introduction
2. Research Methodology
- 1.
- Data Collection and Preprocessing: A dataset consisting of 63 observations of GeC was collected from a quarry mine in Malaysia. The dataset includes 11 input parameters, such as fly ash content, curing temperature, and alkaline activator ratio. These input features were normalized to the range [−1, 1] to ensure consistency in the model training process.
- 2.
- Model Development: Three machine learning models were developed to predict the compressive strength of GeC:
- MLP Model: A basic neural network with varying numbers of hidden layers and neurons, trained using standard backpropagation.
- GOA–MLP Model: The MLP model optimized using the Gannet Optimization Algorithm (GOA) to tune the network’s hyperparameters.
- GWO–MLP Model: The MLP model optimized using the Grey Wolf Optimizer (GWO) to improve the model’s accuracy.
- 3.
- Model Evaluation: The performance of each model was evaluated using R2, Root Mean Squared Error (RMSE), and Variance Accounted For (VAF) during both the training and testing phases. The models were trained on 80% of the data and tested on the remaining 20%. The hybrid models (GOA–MLP and GWO–MLP) were compared to the baseline MLP model to assess improvements in predictive accuracy.
- 4.
- Sensitivity Analysis: Sensitivity analysis was conducted to identify the key parameters influencing GeC’s compressive strength. This analysis helps to highlight the most critical factors that affect the material’s performance and provides insights into the optimal mix design for GeC.
- 5.
- Model Comparison and Discussion: The results were analyzed and compared to highlight the advantages of hybrid optimization algorithms (GOA and GWO) over the standard MLP model in improving prediction accuracy. The implications of the sensitivity analysis were also discussed to guide future research and practical applications of the models.
2.1. Research Techniques
2.1.1. Gannet Optimization Algorithm (GOA)
2.1.2. Grey Wolf Optimizer (GWO)
2.1.3. Artificial Neural Network
2.2. Case Study and Data Analysis
3. Model Development
3.1. MLP
3.2. Development of Hybrid Models
3.2.1. GOA–MLP
3.2.2. GWO–MLP
4. Results and Discussion
5. Sensitivity Analysis
- CA: Ranked 1st, indicating that the calcium activator has the highest sensitivity and strongest influence on compressive strength. This suggests that variations in calcium activator content significantly affect concrete strength.
- FA: Ranked 2nd, showing a high sensitivity to changes in fly ash content. Fly ash is a crucial component in geopolymer concrete, influencing its mechanical properties.
- NaOH/Na2SiO3 Ratio: Ranked 3rd, indicating that the ratio of sodium hydroxide to sodium silicate affects compressive strength considerably. This ratio plays a vital role in geopolymerization reactions.
- CT: Ranked 4th, implying that curing temperature affects compressive strength but to a slightly lesser extent than the aforementioned parameters. Proper curing conditions are essential for achieving desired concrete strength.
- AAB: Ranked 5th, showing its significant but slightly lower impact compared to other parameters.
- M: Ranked 6th, indicating its moderate sensitivity to changes. Modulus of elasticity influences concrete’s ability to deform under stress.
- FAg: Ranked 7th, suggesting its sensitivity to compressive strength, albeit less than other parameters.
- CP: Ranked 8th, indicating that the duration of curing affects compressive strength, but it is relatively less influential compared to other factors.
- RP: Ranked 9th, implying that the presence and amount of reinforcement have a moderate impact on compressive strength.
- Su: Ranked 10th, suggesting its lower sensitivity compared to other parameters. Superplasticizers are used to improve workability but have a relatively minor effect on compressive strength.
- EW: Ranked 11th, indicating it has the least influence on compressive strength among the parameters analyzed.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author | Year | Technique | Number of Data |
---|---|---|---|
Huang et al. [30] | 2021 | SVM | 114 |
Sarir et al. [31] | 2019 | GEP | 303 |
Balf et al. [32] | 2021 | DEA | 114 |
Ahmad et al. [33] | 2021 | GEP, ANN, DT | 642 |
Azimi-Pour et al. [34] | 2020 | SVM | - |
Saha et al. [35] | 2020 | SVM | 115 |
Hahmansouri et al. [36] | 2020 | GEP | 351 |
Hahmansouri et al. [36] | 2019 | GEP | 54 |
Aslam et al. [37] | 2020 | GEP | 357 |
Farooq et al. [38] | 2020 | RF and GEP | 357 |
Asteris and Kolovos [39] | 2019 | ANN | 205 |
Huang et al. [40] | 2019 | IREMSVM-FR withRSM | 114 |
Zhang et al. [41] | 2019 | RF | 131 |
Kaveh et al. [42] | 2018 | M5MARS | 114 |
Sathyan et al. [43] | 2018 | RKSA | 40 |
Vakhshouri and Nejadi [44] | 2018 | ANFIS | 55 |
Belalia Douma et al. [45] | 2017 | ANN | 114 |
Abu Yaman et al. [46] | 2017 | ANN | 69 |
Ahmad et al. [47] | 2021 | GEP, DT and Bagging | 270 |
Farooq et al. [48] | 2021 | ANN, bagging and boosting | 1030 |
Bušić et al. [49] | 2020 | MV | 21 |
Javad et al. [50] | 2020 | GEP | 277 |
Nematzadeh et al. [51] | 2020 | RSM, GEP | 108 |
Parameter | Symbol | Unit | Minimum | Average | Maximum | StD | |
---|---|---|---|---|---|---|---|
1 | Fly ash | FA | (kg/m3) | 298.000 | 401.918 | 430.000 | 39.127 |
2 | Restperiod | RP | (hr) | 0.000 | 14.164 | 72.000 | 14.780 |
3 | Curingtemperature | CT | (°C) | 40.000 | 71.803 | 100.000 | 18.664 |
4 | Curingperiod | CP | (hr) | 24.000 | 27.934 | 48.000 | 8.959 |
5 | NaOH/Na2SiO3 | NaOH/Na2SiO3 | - | 0.300 | 0.400 | 0.500 | 0.027 |
6 | Superplasticizer | Su | (kg/m3) | 0.000 | 4.108 | 10.500 | 4.379 |
7 | Extrawater added | EW | (kg/m3) | 0.000 | 5.738 | 35.000 | 13.065 |
8 | Molarity | M | - | 8.000 | 12.656 | 18.000 | 2.774 |
9 | Alkalineactivator/binder ratio | AAB | - | 0.250 | 0.384 | 0.450 | 0.053 |
10 | Coarseaggregate | CA | (kg/m3) | 875.000 | 1223.915 | 1377.000 | 158.900 |
11 | Fineaggregate | FAg | (kg/m3) | 533.000 | 605.557 | 875.000 | 121.045 |
12 | Compressive strength | GeC | (MPa) | 17.500 | 38.713 | 47.920 | 7.077 |
Model No. | Training | Testing | Training Rates | Testing Rates | Total Rate | Rank | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | VAF | R2 | RMSE | VAF | R2 | RMSE | VAF | R2 | RMSE | VAF | |||
1 | 0.929 | 10.941 | 92.483 | 0.9123 | 11.569 | 59.771 | 4 | 3 | 8 | 7 | 3 | 3 | 28 | 7 |
2 | 0.9486 | 7.255 | 83.183 | 0.8756 | 8.861 | 59.732 | 9 | 9 | 5 | 3 | 9 | 2 | 37 | 4 |
3 | 0.898 | 13.496 | 64.683 | 0.8671 | 13.086 | 54.826 | 1 | 1 | 1 | 2 | 1 | 1 | 7 | 10 |
4 | 0.9498 | 6.132 | 97.71 | 0.9339 | 8.863 | 94.961 | 10 | 10 | 10 | 10 | 8 | 10 | 58 | 1 |
5 | 0.9365 | 10.944 | 72.489 | 0.8643 | 9.547 | 65.395 | 7 | 2 | 2 | 1 | 6 | 5 | 23 | 9 |
6 | 0.9094 | 10.692 | 82.858 | 0.8951 | 10.743 | 64.812 | 2 | 5 | 4 | 6 | 4 | 4 | 25 | 8 |
7 | 0.9196 | 10.705 | 87.583 | 0.8785 | 10.445 | 75.625 | 3 | 4 | 7 | 4 | 5 | 8 | 31 | 5 |
8 | 0.9344 | 8.569 | 95.339 | 0.9171 | 9.491 | 75.572 | 6 | 8 | 9 | 8 | 7 | 7 | 45 | 3 |
9 | 0.9477 | 10.324 | 80.389 | 0.8795 | 11.74 | 66.188 | 8 | 6 | 3 | 5 | 2 | 6 | 30 | 6 |
10 | 0.9341 | 10.164 | 86.772 | 0.9226 | 8.699 | 78.966 | 5 | 7 | 6 | 9 | 10 | 9 | 46 | 2 |
R2 | |||||||
---|---|---|---|---|---|---|---|
Model | Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5 | Mean | Std Dev |
MLP | 0.934 | 0.931 | 0.936 | 0.937 | 0.934 | 0.934 | 0.002 |
GOA–MLP | 0.943 | 0.943 | 0.942 | 0.945 | 0.946 | 0.944 | 0.001 |
GWO–MLP | 0.974 | 0.976 | 0.976 | 0.977 | 0.975 | 0.976 | 0.001 |
RMSE | |||||||
Model | Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5 | Mean | Std Dev |
MLP | 2.448 | 2.449 | 2.45 | 2.447 | 2.451 | 2.449 | 0.001 |
GOA–MLP | 2.248 | 2.249 | 2.25 | 2.247 | 2.251 | 2.249 | 0.001 |
GWO–MLP | 1.431 | 1.432 | 1.433 | 1.43 | 1.434 | 1.432 | 0.001 |
VAF | |||||||
Model | Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5 | VAF | Std Dev |
MLP | 91.266 | 92.266 | 93.266 | 94.266 | 95.266 | 93.266 | 1.291 |
GOA–MLP | 90.862 | 91.862 | 92.862 | 93.862 | 94.862 | 92.862 | 1.291 |
GWO–MLP | 95.507 | 96.507 | 97.507 | 98.507 | 99.507 | 97.507 | 1.291 |
Technique | Train Phase | Test Phase | Train Phase | Test Phase | Total Rate | Rank | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | VAF | R2 | RMSE | VAF | R2 | RMSE | VAF | R2 | RMSE | VAF | |||
MLP | 0.950 | 0.918 | 94.591 | 0.934 | 2.449 | 93.266 | 1 | 2 | 1 | 1 | 2 | 2 | 9 | 3 |
GOA–MLP | 0.963 | 0.811 | 95.781 | 0.944 | 2.249 | 92.862 | 2 | 3 | 2 | 2 | 1 | 1 | 11 | 2 |
GWO–MLP | 0.981 | 0.962 | 97.438 | 0.976 | 1.432 | 97.507 | 3 | 1 | 3 | 3 | 3 | 3 | 16 | 1 |
Comparison | Training Phase | Testing Phase | ||
---|---|---|---|---|
Statistic | p-Value | Statistic | p-Value | |
MLP vs. GOA–MLP (Train) | 493 | 0.0388474 | 29 | 0.0069727 |
MLP vs. GWO–MLP (Train) | 175.5 | 0.0000038 | 34 | 0.0339844 |
GOA–MLP vs. GWO–MLP (Train) | 238 | 0.0001101 | 17 | 0.0122852 |
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Wang, X.; Zhong, Y.; Zhu, F.; Huang, J. Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete. Buildings 2024, 14, 3998. https://doi.org/10.3390/buildings14123998
Wang X, Zhong Y, Zhu F, Huang J. Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete. Buildings. 2024; 14(12):3998. https://doi.org/10.3390/buildings14123998
Chicago/Turabian StyleWang, Xiaoyan, Yantao Zhong, Fei Zhu, and Jiandong Huang. 2024. "Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete" Buildings 14, no. 12: 3998. https://doi.org/10.3390/buildings14123998
APA StyleWang, X., Zhong, Y., Zhu, F., & Huang, J. (2024). Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete. Buildings, 14(12), 3998. https://doi.org/10.3390/buildings14123998