# Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

^{*}

## Abstract

**:**

_{2}SiO

_{3}) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC.

## 1. Introduction

## 2. Machine Learning Methods

#### 2.1. Adaptive Neuro Fuzzy Inference System

#### 2.2. Particle Swarm Optimization

#### 2.3. Genetic Algorithm

**Selection operator:**The best chromosomes of the society are determined by calculating the fitness function of each chromosome, and they are then used as parents to produce offspring. Subsequently, new baby chromosomes then create the next-generation chromosomes.

**Cross operators:**New baby chromosomes born from two parent chromosomes have a better function than their parents. This is due to cross operator method which determines the structure and rate of the chromosome of a child in comparison to the parent chromosome.

**Mutation operator:**This operator searches new areas in available space. The local optimization result may not be accepted as the best solution. To realize this target, it is necessary to randomly change some genes inside the chromosome.

#### 2.4. Quality Assessment

## 3. Preparation of Samples and Data Used

#### 3.1. Materials and Mix Proportion

^{3}) and Na

_{2}SiO

_{3}water glass solution (relative density, RD = 1.42; 39–40.03 wt.% Na

_{2}SiO

_{3}) were purchased from Viet Tri Chemical Factory. The activated alkaline solution was prepared by (i) dissolving NaOH powder in water to obtain NaOH solutions with different molar mass (M), and (ii) mixing with Na

_{2}SiO

_{3}solution in different concentrations. The dissolution and the mixing process are exothermic reactions (temperature ≈ 70 °C). In order to correctly activate fly ash, the solutions were prepared at least one day before mixing into GPC.

_{max}= 19 mm) was sieved following the ASTMC 136 standard and was mixed together to satisfy the ASTMC33 standard. Steel slag aggregates were then divided into two groups: (i) particle size of 4.75–19 mm, acting as coarse aggregates, and (ii) particle size in a 0.15–4.75 mm range, serving as fine aggregates. It is noteworthy that the purchased steel slag aggregates do not naturally satisfy the ASTMC33 standard. The sieved aggregates at 9.5 mm feature 65.83 wt.%, which is out of the range of the ASTMC33 standard (i.e., 20–55%). Therefore, coarse aggregates need to be partially removed from each sieve range, then blended again in order to redistribute the raw material to satisfy such a standard. The aggregate distribution size before and after the mixing process is highlighted in Figure 2 and Figure 3.

#### 3.2. Data Used for Modeling

_{2}SiO

_{3}solution to NaOH solution) were used to predict the compressive strength of the GPC.

#### 3.2.1. Concentration of NaOH Solution (X_{1})

#### 3.2.2. Mass Ratio of Alkali Activating Solution to Fly Ash (X_{2})

#### 3.2.3. Mass Ratio of the Sodium Silicate to the Sodium Hydroxide Solutions (X_{3})

_{2}SiO

_{3}) or potassium silicate (K

_{2}SiO

_{3}), the geopolymerization reaction occurred at a higher rate than when using only alkaline hydroxide [57]. Fernández-Jimenez also showed that the presence of a silicate solution in the alkaline solutions resulted in a significant improvement in the compressive strength of GPC [54]. In fact, NaOH or KOH solutions combined with Na

_{2}SiO

_{3}or K

_{2}SiO

_{3}are the most commonly alkaline activated solutions. The mass ratio of the sodium silicate to the sodium hydroxide solutions (SS/SH) generally determines the properties of the activated alkaline solution. According to the work of Rangan et al., GPC with conventional aggregates could be obtained with the SS/SH ratio of 2.5. When SS/SH increases, there is no significant change in terms of the obtained compressive strength [58]. As there are no previous studies on GPC using steel slag aggregates, such ratios were selected as 2, 2.5, and 3, in order to avoid unexpected situations due to differences in chemical compositions or mechanical properties between steel slag and natural aggregates.

#### 3.2.4. Compressive Strength (Y)

## 4. Modeling Methodology

_{2}SiO

_{3}to solution NaOH, and compressive strength), were divided into two parts. The first part included 70% of the data which were then used to construct the models. The second part consisted of 30% of the remaining data which were then used to validate the models.

## 5. Results and Discussion

_{1}(molar mass of the NaOH solution), X

_{2}(mass ratio between alkaline activated solution and fly ash), and X

_{3}(mass ratio between the sodium silicate and the sodium hydroxide solutions), as input variables. The corresponding compressive strength of samples was used as the target variable (Y) for the prediction modeling, and they were determined from a compressive strength test of 210 GPC samples.

## 6. Conclusions

_{2}SiO

_{3}to solution NaOH (SS/SH) (2, 2.5, and 3) as the input parameters. Validation and comparison of the models was done using various error determination criteria, including MAE, RSME, and R. The results show that the proposed models performed well for the prediction of the compressive strength of GPC, but the PSOANFIS model (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) outperformed the GAANFIS model (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). Thus, it can be reasonably concluded that the proposed hybrid AI approach of PSOANFIS is a promising method for the prediction of the compressive strength of GPC. This study would help in reducing the time and cost of construction, as well as in the improvement of the environment. However, a limitation of this study is that we considered only ingredients that combine to form geopolymer binders in predicting the compressive strength of GPC, and we did not consider the quality, quantity, or properties of aggregates which might affect the compressive strength of GPC. Thus, this is proposed to carry out a study of these factors in future modeling. In addition, a sensitivity analysis of different combinations of input data may also be carried out to test the importance of each input parameter for better modeling of the compressive strength of GPC.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**(

**a**) Samples of geopolymer concrete (GPC) using steel slag aggregate; (

**b**) forced mixing concrete mixer; (

**c**) compressive strength test of the specimen; (

**d**) fracture mode.

**Figure 6.**Experimental and predicted values of compressive strength with different models: (

**a**) training particle swarm optimization (PSO)-based ANFIS (PSOANFIS), (

**b**) testing PSOANFIS, (

**c**) training genetic algorithm (GA)-based ANFIS (GAANFIS), and (

**d**) testing GAANFIS.

**Figure 7.**Correlation coefficients of different models: (

**a**) training PSOANFIS, (

**b**) testing PSOANFIS, (

**c**) training GAANFIS, and (

**d**) testing GAANFIS.

**Figure 8.**Error distribution of different models: (

**a**) training PSOANFIS, (

**b**) testing PSOANFIS, (

**c**) training GAANFIS, and (

**d**) testing GAANFIS.

**Figure 9.**Mean errors and standard deviations of different models: (

**a**) training PSOANFIS, (

**b**) testing PSOANFIS, (

**c**) training GAANFIS, and (

**d**) testing GAANFIS.

SiO_{2} | Al_{2}O_{3} | Fe_{2}O_{3} | CaO | MgO | K_{2}O | Na_{2}O | TiO_{2} | SO_{3} | LOI * |
---|---|---|---|---|---|---|---|---|---|

51.74 | 24.53 | 5.59 | 0.81 | 1.95 | 4.42 | 0.11 | 0.76 | 0.31 | 8.98 |

Size range (μm) | 30 | 20 | 10 | 5 |

Passing Sieved volume (wt.%) | 95 | 51.67 | 33.06 | 16.77 |

**Table 3.**Experimental results determining the effect of alkali activated solution to fly ash (AAS/FA) ratio.

AAS/FA | Workability | Compressive Strength (MPa) |
---|---|---|

0.30 | Hard | 58 |

0.35 | Moderate | 45 |

0.40 | Moderate | 37 |

0.45 | High slump | 32 |

Mixture No. | X_{1} (M) | X_{2} | X_{3} | Y (MPa) |
---|---|---|---|---|

GPC 01 | 10 | 0.4 | 2 | 40.17 |

GPC 02 | 14 | 0.4 | 2 | 49.43 |

GPC 03 | 10 | 0.5 | 2 | 29.73 |

GPC 04 | 14 | 0.5 | 2 | 43.17 |

… | … | … | … | … |

… | … | … | … | … |

… | … | … | … | … |

GPC 206 | 12 | 0.45 | 3 | 35.33 |

GPC 207 | 12 | 0.45 | 2.5 | 42.47 |

GPC 208 | 14 | 0.40 | 3 | 48.59 |

GPC 209 | 14 | 0.5 | 3 | 36.28 |

GPC 210 | 14 | 0.45 | 2.5 | 49.39 |

Statistical Parameters | PSOANFIS | GAANFIS | ||
---|---|---|---|---|

Training Set | Testing Set | Training Set | Testing Set | |

RMSE | 3.269 | 2.251 | 3.115 | 2.531 |

R | 0.856 | 0.934 | 0.869 | 0.927 |

MAE | 2.236 | 1.847 | 2.293 | 2.115 |

Mean error | −0.024 | −0.649 | −0.004 | −0.602 |

SD | 3.281 | 2.173 | 3.126 | 2.479 |

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**MDPI and ACS Style**

Dao, D.V.; Trinh, S.H.; Ly, H.-B.; Pham, B.T.
Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. *Appl. Sci.* **2019**, *9*, 1113.
https://doi.org/10.3390/app9061113

**AMA Style**

Dao DV, Trinh SH, Ly H-B, Pham BT.
Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. *Applied Sciences*. 2019; 9(6):1113.
https://doi.org/10.3390/app9061113

**Chicago/Turabian Style**

Dao, Dong Van, Son Hoang Trinh, Hai-Bang Ly, and Binh Thai Pham.
2019. "Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches" *Applied Sciences* 9, no. 6: 1113.
https://doi.org/10.3390/app9061113