A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches
Abstract
:1. Introduction
2. Literature Review
2.1. Artificial Neural Network
2.1.1. Levenberg–Marquardt Algorithm
2.1.2. Bayesian Regularization
2.1.3. Scaled Conjugate Gradient Backpropagation
3. Research Significance
4. Materials and Methods
4.1. Experimental Plan
4.2. Data Visualization
4.2.1. By Frequency Distribution
4.2.2. By Multi-Correlation Graph (Heat Map)
4.3. Methodology of Artificial Neural Network Model
4.4. ANN Network Model Assessment
5. Results and Discussion
5.1. Levenberg–Marquardt Algorithm
5.2. Bayesian Regularization
5.3. Scaled Conjugate Gradient Backpropagation
5.4. Comparison of Algorithms
5.5. Sensitivity Analysis
6. Conclusions
- In developing LM, BR, and SCG models, a total of 515 samples were acquired from research papers and randomly distributed into 70%, 10%, and 20% for training (360), validation (52), and testing (103), respectively. Due to the built-in validation mechanism in the training stage of the BR algorithm, the ratio became 80% for training and 20% for testing.
- For the present study, three algorithms, LM, BR, and SCG, were trained and evaluated, giving an overall accuracy of 90%, 91%, and 70%, respectively, with MSE values of 48.14, 43.75, 113.42. The SCG algorithm is the worst model for forecasting SCC compressive strength, with RA having poor correlation and mean squared error.
- Bayesian regularization gives better results than LM and SCG, with the highest coefficient of correlation (R = 91%) and the lowest MSE (43.75). However, in the meantime, the LM algorithm also gave nearly the same coefficient of correlation (R = 90%) with a much shorter processing time than the BR algorithm.
- The findings demonstrated that the LM and BR algorithms are suitable models and can be adapted to predict the 28 days compressive strength of self-compacting concrete amended with recycled aggregates.
- According to the model’s sensitivity analysis, the most significant parameter determining compressive strength is cement, contributing 28.39%. Water, with a contribution of 23.47%, is another crucial variable in predicting compressive strength in the same setting. The variable with the lowest occurrence, on the other hand, was coarse aggregate (9.23%). All the data suggest that cement and water improve the compressive strength of SCC with RA, but coarse aggregate reduces it. Admixture, fine aggregates, and superplasticizers, on the other hand, play a minor role in the development of the model.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No | Reference | # Mix | % Data | No | Reference | # Mix | % Data |
---|---|---|---|---|---|---|---|
1 | Ali et al., 2012 [49] | 18 | 3.50 | 28 | Nili et al. [50] | 10 | 1.94 |
2 | Aslani et al., 2018 [51] | 15 | 2.91 | 29 | Pan et al., 2019 [52] | 6 | 1.17 |
3 | Babalola et al., 2020 [53] | 14 | 2.72 | 30 | Pereira-de-Oliveira et al., 2014 [54] | 4 | 0.78 |
4 | Bahrami et al., 2020 [55] | 10 | 1.94 | 31 | Poongodi et al., 2020 [56] | 9 | 1.75 |
5 | Barroqueiro et al. [57] | 6 | 1.17 | 32 | Revathi et al., 2013 [58] | 5 | 0.97 |
6 | Behera et al., 2019 [59] | 6 | 1.17 | 33 | Revilla Cuesta et al., 2020 [60] | 5 | 0.97 |
7 | Bidabadi et al., 2020 [61] | 11 | 2.14 | 34 | Sadeghi-Nik et al., 2019 [62] | 12 | 2.33 |
8 | Chakkamalayath et al., 2020 [63] | 6 | 1.57 | 35 | Salesa et al., 2017 [64] | 4 | 0.78 |
9 | Duan et al., 2020 [65] | 10 | 1.94 | 36 | Sasanipour et al., 2019 [66] | 10 | 1.94 |
10 | Fiol et al., 2018 [67] | 12 | 2.33 | 37 | Señas et al., 2016 [9] | 6 | 1.17 |
11 | Gesoglu et al., 2015 [68] | 24 | 4.66 | 38 | Sharifi et al., 2013 [69] | 6 | 1.17 |
12 | Grdic et al., 2010 [70] | 3 | 0.58 | 39 | Ali and Al-Tersawy, 2014 [49] | 15 | 2.91 |
13 | Guneyisi et al., 2014 [71] | 5 | 0.97 | 40 | Silva et al., 2016 [72] | 15 | 2.91 |
14 | Guo et al., 2020 [73] | 27 | 5.24 | 41 | Singh et al., 2019 [74] | 12 | 2.33 |
15 | Kapoor et al., 2016 [75] | 8 | 1.55 | 42 | Sua-iam et al., 2013 [76] | 20 | 3.88 |
16 | Katar et al., 2021 [77] | 4 | 0.78 | 43 | Sun et al., 2020 [78] | 10 | 1.94 |
17 | Khodair et al., 2017 [79] | 20 | 3.88 | 44 | Surendar et al., 2021 [80] | 7 | 1.36 |
18 | Kou et al., 2009 [81] | 13 | 2.52 | 45 | Tang et al., 2016 [82] | 5 | 0.97 |
19 | Krishna et al., 2018 [83] | 5 | 0.97 | 46 | Thomas et al., 2016 [84] | 4 | 0.78 |
20 | Kumar et al., 2017 [85] | 4 | 0.78 | 47 | Tuyan et al., 2014 [86] | 12 | 2.33 |
21 | Li et al., [87] | 4 | 0.78 | 48 | Uygunoglu et al., 2014 [88] | 8 | 1.55 |
22 | Long et al., 2016 [89] | 4 | 0.78 | 49 | Wang et al., 2020 [90] | 5 | 0.97 |
23 | Mahakavi and Chithra, 2019 [91] | 25 | 4.85 | 50 | Yu et al., 2014 [92] | 3 | 0.58 |
24 | Manzi et al., 2017 [93] | 4 | 0.78 | 51 | Yu et al., 2020 [94] | 3 | 1.14 |
25 | Martínez-García et al., 2020 [95] | 4 | 0.78 | 52 | Yu et al., 2021 [96] | 21 | 4.08 |
26 | Mo et al., 2021 [97] | 5 | 0.97 | 53 | Zhou et al., 2013 [98] | 6 | 1.17 |
27 | Nieto et al., 2018 [99] | 22 | 4.27 | Total | 515 | 100 |
Variables | Abbreviation | Minimum | Mean | Maximum | |
---|---|---|---|---|---|
Input | Cement (kg/m3) | C | 78.00 | 375.84 | 635.00 |
Mineral Admixture (kg/m3) | MA | 0.00 | 135.17 | 515.00 | |
Water (kg/m3) | W | 45.50 | 176.87 | 277.00 | |
Fine Aggregates (kg/m3) | FA | 532.20 | 845.10 | 1200.00 | |
Coarse Aggregates (kg/m3) | CA | 328.00 | 784.91 | 1170.00 | |
Superplasticizer (kg/m3) | SP | 0.00 | 4.50 | 16.00 | |
Output | Compressive Strength (MPa) | f’c | 7.17 | 44.94 | 87.00 |
Levenberg–Marquardt Algorithm | ||
---|---|---|
Phase | Percentage (%) | No. of Specimens |
Training | 70 | 360 |
Validation | 10 | 52 |
Testing | 20 | 103 |
Total | 100 | 515 |
Bayesian Regularization | ||
Training | 80 | 412 |
Validation | - | 0 |
Testing | 20 | 103 |
Total | 100 | 515 |
Scaled Conjugate Gradient Backpropagation | ||
Training | 70 | 360 |
Validation | 10 | 52 |
Testing | 20 | 10 |
Total | 100 | 515 |
Phase | Function | MSE | R |
---|---|---|---|
Training | trainLM | 32.41 | 0.921 |
Validation | trainLM | 61.60 | 0.823 |
Testing | trainLM | 50.42 | 0.890 |
Total | trainLM | 48.14 | 0.902 |
Phase | Function | MSE | R |
---|---|---|---|
Training | trainbr | 38.17 | 0.9154 |
Testing | trainbr | 49.34 | 0.8923 |
Total | trainbr | 43.755 | 0.9100 |
Phase | Function | MSE | R |
---|---|---|---|
Training | trainSCG | 95.62 | 0.732 |
Validation | trainSCG | 104.36 | 0.704 |
Testing | trainSCG | 140.27 | 0.581 |
Total | trainSCG | 113.42 | 0.701 |
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de-Prado-Gil, J.; Palencia, C.; Jagadesh, P.; Martínez-García, R. A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches. Materials 2022, 15, 5232. https://doi.org/10.3390/ma15155232
de-Prado-Gil J, Palencia C, Jagadesh P, Martínez-García R. A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches. Materials. 2022; 15(15):5232. https://doi.org/10.3390/ma15155232
Chicago/Turabian Stylede-Prado-Gil, Jesús, Covadonga Palencia, P. Jagadesh, and Rebeca Martínez-García. 2022. "A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches" Materials 15, no. 15: 5232. https://doi.org/10.3390/ma15155232