# Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms

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## Abstract

**:**

_{2}) emission into the atmosphere is mostly the result of human-induced activities and causes dangerous environmental impacts by increasing the average temperature of the earth. Since the production of ordinary Portland cement (PC) is a major contributor to CO

_{2}emissions, this study proposes alkali-activated binders as an alternative to reduce the environmental impact of ordinary Portland cement production. The dataset required for the training processes of these algorithms was created using Mendeley as a data-gathering instrument. Some of the most efficient state-of-the-art meta-heuristic optimization algorithms were applied to obtain the optimal neural network architecture with the highest performance. These neural network models were applied in the prediction of carbon emissions. The accuracy of these models was measured using statistical measures such as the mean squared error (MSE) and coefficient of determination (R

^{2}). The results show that carbon emissions associated with the production of alkali-activated concrete can be predicted with high accuracy using state-of-the-art machine learning techniques. In this study, in which the binders produced by the alkali activation method were evaluated for their usability as a binder material to replace Portland cement, it is concluded that the most successful hyperparameter optimization algorithm for this study is the genetic algorithm (GA) with accurate mean squared error (MSE = 161.17) and coefficient of determination (R

^{2}= 0.90) values in the datasets.

## 1. Introduction

_{2}) in the atmosphere which is caused by factors such as the rapid increase in the world population, unplanned destruction of the natural structure due to human actions and increasing industry and fuel use, as well as the rapid increase in greenhouse gas accumulations such as methane (CH

_{4}), nitrous oxide (N

_{2}O), etc., which strengthen the natural greenhouse effect [1]. Global warming, the impact of which is felt more and more day by day, brings along many problems. If global warming, which has reached threatening dimensions all over the world, cannot be prevented, future generations will face many global problems that cannot be compensated [2].

_{2}) emissions increased by 0.9% (321 Mt), reaching 36.8 Gt, the highest CO

_{2}emission value of a certain time as seen in Figure 1 [3].

_{2}) released into the atmosphere during the production of Portland cement (PC) is one of the important factors causing global warming. The production of Portland cement consumes large amounts of energy and raw materials and emits large amounts of CO

_{2}, which contributes to global warming [4].

_{2}emissions is dependent on carbon-emitting by-products in clinker production. There is also a significant amount of CO

_{2}emission during the calcination process [7]. As a result of the investigations, it has been determined that each tonne of Portland cement produced releases almost one tonne of CO

_{2}into the atmosphere [8]. Since concrete is known to be the second most used substance in the world after water [9], this means a very high carbon dioxide emission.

_{2}it emits and its production consumes high energy, it has become necessary to replace cement with alternative binding materials with similar functions.

_{2}emissions in cement production and to use resources more efficiently, the use of alternative materials is increasing. For the sustainability of cementitious materials, studies using construction waste dust [12] or rock dust [13] as a cement substitute have been carried out, and successful results have been obtained. In this study, instead of ordinary Portland cement (OPC), which requires a high level of energy for its production that is highly damaging to the environment, materials that can be activated with alkalis (alkali-activated binders) can be activated by an activator and converted into a binder.

_{2}) emission in the production of cement make it attractive to investigate alternative binders instead of cement. In this study, alkaline-activated binders are proposed as an alternative to OPC. In this way, CO

_{2}emission can be significantly reduced. This research is important as efforts to address climate change issues can have a significant impact on the cement-producing industry.

_{2}emissions caused by AAM are lower than Portland cement [22].

_{2}emission of the alkali-activated concrete proposed as an alternative to Portland cement in a time-efficient and practical way by using machine learning without the need for a mathematical relationship between the problem and parameters.

_{2}and nitrous oxide (N

_{2}O) emissions from agricultural land. The classical regression models they used in their study adequately simulated the cyclical and seasonal variations of CO

_{2}fluxes (R = 0.75, 0.71 and 0.68, respectively). Leerbeck et al. [24] developed a machine learning algorithm to predict CO

_{2}emission intensities. For the analysis of the dataset collected from the Danish tender region, three linear regression models and Softmax were combined into a final model using weighted averaging, resulting in a small NRMSE (0.095 to 0.183). Li and Sun [25] used machine learning methods to estimate city-level CO

_{2}emissions in China. Among the ML models, XGBoost reached the highest prediction accuracy (R

^{2}> 0.98). Li et al. [26] used three machine learning algorithms, namely ordinary least squares regression (OLS), support vector machine (SVM) and gradient boosting regression (GBR), to estimate CO

_{2}emissions from transport. The GBR model achieved the best result with an R

^{2}of 0.9943. Wang et al. [27] used machine learning models to predict CO

_{2}emissions in China. They achieved the lowest prediction error with a two-stage support vector regression-artificial neural network (SVR-ANN).

_{2}emission minimization. He et al. [28] addressed an effective way to reduce carbon emissions by using steel slag for CO

_{2}sequestration and generated a dataset on the carbonation reactivity of steel slag through machine learning with SHapley Additive Explanations (SHAP). They achieved successful results using multilayer perceptron (MLP), random forest and support vector regression models to predict CO

_{2}sequestration. Amin et al. [29] aimed to reduce CO

_{2}emissions by using waste eggshells in cement-based materials and used learning (ML) to evaluate the flexural strength (FS) of cement-based materials containing eggshell powder (ESP). The results showed that machine learning techniques can be used to evaluate material properties in the construction industry. Wang et al. [30] created a hybrid machine learning model with optimization for building design to reduce CO

_{2}emission. The results of the optimization show that the hybrid model used provides a reduction in CO

_{2}emission (11.06%). Yücel et al. [31] studied the minimum carbon dioxide (CO

_{2}) emission of a simply supported reinforced concrete (RC) beam with a rectangular cross-section using artificial neural networks (ANNs). The results showed that an environmentally friendly design can be achieved. Bekdaş et al. [32] performed an optimization process to create an environmentally friendly structural model for an axisymmetric reinforced concrete cylindrical wall with post-tensioning for CO

_{2}minimization. As a result, they found that increasing the number of post-tensioning loads in the optimum design reduces CO

_{2}emissions. Aydın et al. [33] used the harmony search (HS) algorithm and different regression models as prediction models for an engineering design to reduce CO

_{2}emissions. The results showed that the random forest algorithm has good performance. Sun et al. [34] used a random forest machine learning algorithm for the optimization and prediction of alkali-activated concrete to reduce CO

_{2}emission. The machine learning model used provides practical information on the state of the art in alkali-activated concrete mix design. Cakiroglu and Bekdaş [35] aimed to minimize CO

_{2}emissions associated with the production of the plate beam. In their study, they used the meta-heuristic Jaya algorithm as the optimization method.

_{2}emission associated with the production of alkali-activated concrete. The study aims to contribute to the production of environmentally friendly and sustainable binder materials with very low CO

_{2}emission and production energy, which can be used as ordinary Portland cement substitutes.

## 2. Materials and Methods

#### 2.1. The Dataset

_{2}prediction are given in Table 1. The Pearson correlation coefficients between different data features are shown in Figure 3 in a color-coded way. In Figure 3, a high linear correlation between the features is shown with the shades of blue, whereas inverse correlations are shown with the shades of red.

#### 2.2. Using HyperNetExplorer

#### 2.2.1. Artificial Neural Network (ANN)

#### 2.2.2. Algorithms for Hyperparameter Optimization

#### CMAES (Covariance Matrix Adaptation Evolution Strategy)

^{g}), step size (σ

^{g}) and covariance matrix (C

^{g}) in Equation (2). In Equation (2), g is the population algebra.

#### Genetic Algorithm (GA)

_{q,new}is new values of qth parameter, X

_{q,min}is lower limit value of qth parameter, and X

_{q,max}is upper limit value of qth parameter. rand() is random number between 0 and 1.

#### Original Particle Swarm Optimization (PSO)

_{best}) from the current generation is found. The number of the best in the herd is equal to the number of particles. The global best (g

_{best}) is selected from the local best in the current generation. Position and velocities are restored using Equation (4) [59]. Equation (5) gives the new position value.

_{i,j}gives position and V

_{i,j}speed values, while rand() is a randomly generated number between 0 and 1. The steps from step 2 onward are repeated until the stopping criterion is met.

#### 2.2.3. Performance Evaluation

^{2}coefficient were used to evaluate and compare the results.

^{2}) is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination or coefficient of multiple determination for multiple regression [61]. The formula of R

^{2}is given in Equation (7).

#### 2.2.4. HyperNetExplorer: Architecture and Operation

^{2}) are provided as a table in the Streamlit-based GUI. Once training is complete, all ANN models are stored on the server and can be downloaded as (*.pt) files.

## 3. Results and Discussion

_{2}prediction from the MealPy [37] package were tested. HyperNetExplorer managed to discover ANN architectures for the CO

_{2}dataset. The general scheme of the study is shown in Figure 6.

^{2}range of the 10 best-performing ANN architectures is 0.85–0.88.

^{2}range of the 10 best-performing ANN architectures is 0.89–0.9.

^{2}range of the 10 best-performing ANN architectures is 0.76–0.984.

^{2}value, has the lowest error rate. The highest error rate is in particle swarm optimization (PSO). A low error rate means that the actual value and predicted values are close to each other. This shows that the prediction model is effective in terms of success and reliability. The higher the error rate, the further away it is from the prediction that is considered correct. As can be seen in Figure 10, the smallest difference between the actual and predicted value is in the GA while the biggest difference is in PSO.

_{2}dataset using CMAES, GA and PSO optimizers. The best MSE and R

^{2}achieved for each optimizer is given in Table 6.

^{2}) with hyperparameter optimization is the genetic algorithm (GA). The result obtained with the GA is better than the mean squared error and coefficient of determination obtained after regression analysis with hyperparameter optimization with covariance matrix adaptation evolution strategy (CMAES) and particle swarm optimization (PSO). After the GA, CMAES (MSE = 187.07 and R

^{2}= 0.88) showed the best performance. PSO (MSE = 271.59 and R

^{2}= 0.84) showed the lowest performance.

## 4. Conclusions

^{2})) for a CO

_{2}prediction dataset.

- (1)
- Hyperparameter optimization with the genetic algorithm showed successful regression performance with accurate mean squared error (MSE = 161.17) and coefficient of determination (R
^{2}= 0.90) values in the datasets. - (2)
- CMAES follows the GA with MSE = 187.07 and R
^{2}= 0.88. - (3)
- The algorithm with the lowest R
^{2}(R^{2}= 0.84) value and the highest MSE (MSE = 271.59) among them is PSO.

_{2}emission associated with alkali-activated concrete production. This study contributes to the production of environmentally friendly and sustainable binder materials with very low CO

_{2}emissions and production energy that can be used as ordinary Portland cement (OPC) substitutes.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**Production of cement in India by year [11].

**Figure 4.**Artificial neural networks consisting of artificial nerve cells [42].

**Figure 5.**Activation functions [46].

Predictor | Min–Max Value | Mean | Standard Deviation |
---|---|---|---|

SiO_{2} | 30.61–77.1 | 50.166 | 9.377 |

Al_{2}O_{3} | 4.26–38.38 | 23.365 | 6.710 |

Fe_{2}O_{3} | 0.3–17.86 | 4.465 | 2.888 |

CaO | 0.05–43.34 | 12.993 | 11.802 |

MgO | 0–9.57 | 3.028 | 2.629 |

Na_{2}O | 0–3.66 | 0.434 | 0.552 |

K_{2}O | 0–5.03 | 0.877 | 1.111 |

SO_{3} | 0–5.04 | 0.676 | 0.793 |

TiO_{2} | 0–2.19 | 0.560 | 0.723 |

P_{2}O_{5} | 0–4.48 | 0.200 | 0.655 |

SrO | 0–0.5 | 0.001 | 0.007 |

Mn_{2}O_{3} | 0–0.29 | 0.010 | 0.039 |

MnO | 0–0.37 | 0.012 | 0.050 |

LOI | 0–13.97 | 1.254 | 1.474 |

kg of binder per m^{3} of mix | 150–788.58 | 402.938 | 88.017 |

Coarse aggregate (kg/m^{3}) | 525.4–1591.34 | 1093.954 | 181.134 |

Fine aggregate (kg in 1 m^{3} mix) | 318.27 | 669.847 | 122.338 |

Total aggregates (kg in 1 m^{3} mix) | 1110 | 1763.798 | 150.755 |

Total Na_{2}SiO_{3} (kg in 1 m^{3} of mix) | 49.6–213 | 123.887 | 31.492 |

Na_{2}O (L)% | 0.08–0.23 | 0.139 | 0.027 |

SiO_{2} (L)% | 0.21–0.35 | 0.303 | 0.031 |

H_{2}O% | 0.48–0.64 | 0.558 | 0.046 |

Na_{2}O (Dry) | 6.08–35.18 | 16.843 | 5.280 |

SiO_{2} (Dry) | 11.23–66.84 | 37.162 | 10.947 |

Water | 25.26–130.51 | 67.967 | 19.300 |

Total NaOH (kg in 1 m^{3} mix) | 22.5–133.18 | 59.078 | 17.977 |

Concentration (M) NaOH | 3–22 | 10.927 | 3.112 |

Water | 8.51–79.91 | 33.217 | 12.243 |

NaOH (Dry) | 2.98–85.24 | 25.861 | 11.628 |

Superplasticizer (kg in 1 m^{3} mix) | 0–47 | 5.838 | 7.246 |

Total water (in solutions + additional) (kg in 1 m ^{3} mix) | 41.38–303.54 | 124.053 | 49.615 |

Cube D (mm) | 50–150 | 121.267 | 27.337 |

fc_{cube} (MPa) | 3–91.94 | 45.554 | 15.256 |

Target | |||

CO_{2} footprint (kg emision per 1 m ^{3} of samples) | 38.23–895.07 | 154.473 | 86.962 |

Parameter Name | Lower Bound | Upper Bound | Options |
---|---|---|---|

Number of Hidden Layers (HLs) | 0 | 2 | 0: Single HL |

1: Two HL | |||

2: Three HL | |||

Number of Neurons in HL = 1 | 0 | 6 | 0: 8 |

1: 16 | |||

2: 32 | |||

3: 64 | |||

4: 128 | |||

5: 256 | |||

6: 512 | |||

Number of Neurons in HL = 2 | 0 | 6 | 0: 8 |

1: 16 | |||

2: 32 | |||

3: 64 | |||

4: 128 | |||

5: 256 | |||

6: 512 | |||

Number of Neurons in HL = 3 | 0 | 6 | 0: 8 |

1: 16 | |||

2: 32 | |||

3: 64 | |||

4: 128 | |||

5: 256 | |||

6: 512 | |||

Activation Function of HL = 1 | 0 | 6 | 0: LeakyReLU |

1: Sigmoid | |||

2: Tanh | |||

3: ReLU | |||

4: LogSigmoid | |||

5: ELU | |||

6: Mish | |||

Activation Function of HL = 2 | 0 | 6 | 0: LeakyReLU |

1: Sigmoid | |||

2: Tanh | |||

3: ReLU | |||

4: LogSigmoid | |||

5: ELU | |||

6: Mish | |||

Activation Function of HL = 3 | 0 | 6 | 0: LeakyReLU |

1: Sigmoid | |||

2: Tanh | |||

3: ReLU | |||

4: LogSigmoid | |||

5: ELU | |||

6: Mish |

Neural Network Structure | Parameters |
---|---|

Type of optimization method | CMAES |

Number of layers in the network | 5 |

Number of neurons in the input layer | 33 |

Number of hidden layers | 3 |

Number of hidden layer neurons | 512-256-128 |

Total number of iterations | 1050 |

Number of best iteration | 699 |

Mean squared error (MSE) | 187.07 |

Coefficient of determination (R^{2}) | 0.88 |

Neural Network Structure | Parameters |
---|---|

Type of optimization method | Genetic Algorithm |

Number of layers in the network | 5 |

Number of neurons in the input layer | 33 |

Number of hidden layers | 3 |

Number of hidden layer neurons | 512-256-128 |

Total number of iterations | 1050 |

Number of best iteration | 1030 |

Mean squared error (MSE) | 161.17 |

Coefficient of determination (R^{2}) | 0.9 |

Neural Network Structure | Parameters |
---|---|

Type of optimization method | Particle Swarm Optimization |

Number of layers in the network | 5 |

Number of neurons in the input layer | 33 |

Number of hidden layers | 3 |

Number of hidden layer neurons | 512-512-256 |

Total number of iterations | 1050 |

Number of best iteration | 955 |

Mean squared error (MSE) | 271.59 |

Coefficient of determination (R^{2}) | 0.84 |

Optimizer | MSE | R^{2} | Number of Best Iteration |
---|---|---|---|

CMAES | 187.07 | 0.88 | 699 |

GA | 161.17 | 0.90 | 1030 |

PSO | 271.59 | 0.84 | 955 |

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## Share and Cite

**MDPI and ACS Style**

Aydın, Y.; Cakiroglu, C.; Bekdaş, G.; Işıkdağ, Ü.; Kim, S.; Hong, J.; Geem, Z.W.
Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms. *Sustainability* **2024**, *16*, 142.
https://doi.org/10.3390/su16010142

**AMA Style**

Aydın Y, Cakiroglu C, Bekdaş G, Işıkdağ Ü, Kim S, Hong J, Geem ZW.
Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms. *Sustainability*. 2024; 16(1):142.
https://doi.org/10.3390/su16010142

**Chicago/Turabian Style**

Aydın, Yaren, Celal Cakiroglu, Gebrail Bekdaş, Ümit Işıkdağ, Sanghun Kim, Junhee Hong, and Zong Woo Geem.
2024. "Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms" *Sustainability* 16, no. 1: 142.
https://doi.org/10.3390/su16010142