# Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

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## Abstract

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^{2}values (0.87 for the ANN, 0.81 for the KNN, and 0.78 for LR), as well as the assessment of variation between test values and anticipated outcomes and errors (1.32% for ANN, 1.57% for KNN, and 1.69% for LR), it was determined that the accuracy of the ANN model was superior to the KNN and LR. The sieve diagram exhibited a correlation amongst the model predicted and target results. The outcomes of the RreliefF analysis suggested that ESP and RGP significantly influenced the CS loss of samples with RreliefF scores of 0.26 and 0.21, respectively. Based on the outcomes of the research, the ANN approach was determined suitable for predicting the CS loss of mortar subjected to acidic environments, thereby eliminating lab testing trails.

## 1. Introduction

_{2}SO

_{4}, is a highly corrosive acid that can cause structural corrosion [8]. The corrosive nature of H

_{2}SO

_{4}may substantially threaten construction materials, leading to accelerated degradation and even structural failure. The alkaline nature of the pore water in cementitious compounds leads to a reaction with mild or strong acids. This reaction involves the calcium-silicate-hydrate (CSH) gel and Ca(OH)

_{2}, present in the cement matrix, generating dissolved ions. Consequently, this dissolution process leads to the breakdown of concrete components [5]. H

_{2}SO

_{4}is widely recognized as a highly hazardous substance when it interacts with cement-based materials due to its corrosive effect, further exacerbated by sulfate ions [8]. The degradation of cementitious composites and its impacts on durability is significantly influenced by sulfate attacks caused by the physical and chemical assault of sulfates and salt crystallization [9]. The durability issues in cement-based compounds can be attributed to the decalcification of CSH and the subsequent formation of other compounds [9]. Hence, it is essential to thoroughly examine the potential degradation of cement-based materials resulting from exposure to acid during their operational lifespan.

_{2}SO

_{4}solution for 90 days. During this period, CS and flexural strength (FS) changes were recorded. Using ESP proportion as a substitute for sand reduced the CS and FS of submerged samples when exposed to the sulfate solution. The composite exhibited diminished strength performance due to the ESP’s worse strength features than sand. In their study, Wei et al. [14] inspected the effects of incorporating unprocessed ESP into cement mortar. Specifically, they examined the changes in weight and CS deterioration after the mortar specimens were exposed to a 5% Na

_{2}SO

_{4}solution. Using ESP led to increased weight and CS loss, contrary to the control specimen. Moreover, the CS and weight reduction were more pronounced when the ESP proportion increased as a cement substitute. The available research demonstrates that applying unprocessed ESP in cement-based materials diminishes the capacity to withstand acid attack. Nevertheless, reports have indicated that using treated ESP, such as calcinated ESP, along with the incorporation of a pozzolanic compound, could potentially enhance the properties of cement-based compounds [15]. The process of obtaining treated ESP is shown in Figure 1.

_{2}) emissions, making a significant contribution to global warming [17]. Cement companies can reduce costs and CO

_{2}emissions by implementing waste recycling and reuse practices [18]. Hence, a significant need exists for environmentally sustainable cement-based materials within the building sector [19,20]. In addition, extracting natural aggregates produces a significant amount of CO

_{2}and contributes to the diminution of natural resources [21]. Recycled glass powder (RGP) modified cement-based composites are increasingly attractive in the building industry, primarily attributed to their cost-effectiveness and wide accessibility. The usage of RGP in cement or sand at a substitution rate of 10–20% has been shown to enhance mechanical qualities, specifically CS and FS [22,23]. The study of Qaidi et al. [24] gathered various investigations that incorporated RGP as a partial or full alternative of cement to assess its hardened and fresh properties. The research of Zeybek, Ö. et al. [25] claimed that substituting 20% of RGP in cement showed optimum results for the mechanical properties of RGP-based concrete. Furthermore, it is concluded by the study that a 10% substitution of RGP with aggregates showed optimum results. Çelik et al. [26] used various proportions of RGP (i.e., 10% to 50%) as an alternative to fine and coarse aggregates. The conclusion of the study suggests that a 20% replacement of RGP showed optimum results. Karalar et al. [27] have studied the flexural behaviors of waste marble-based (WMP) concretes. The results suggest that when the quantity of WMP in the concrete mix is raised from 0% to 40%, it is observed that the nature of the crack shifts from shear crack to flexural crack as the proportion of WMP increases in the mix ratio. The research of Özkılıç, Y.O. et al. [28] utilized WG with fly-ash in geopolymer concrete to assess its fresh and mechanical properties. It is recommended by the study to incorporate 10% glass aggregate with a NaOH molarity of 16 in order to achieve the most sustainable GPC, taking into account both fresh and mechanical attributes. Çelik, A.İ. et al. [29] studied the behavior of incorporating RGP with fly ash in geopolymer concrete. Consequently, substituting sand with RGP offers potential benefits, such as the conservation of natural resources, improved waste management, and reduced cement consumption and CO

_{2}emissions [30].

## 2. Research Methodology

#### 2.1. Data

#### 2.2. Machine Learning Algorithms

#### 2.2.1. Artificial Neural Network

#### 2.2.2. K-Nearest Neighbors

#### 2.2.3. Linear Regression

_{j}correspond to the features or inputs of the dataset. Additionally, the parameters x

_{0}, x

_{1}, …, x

_{m}are the values that need to be trained to optimize the model.

#### 2.3. Validation of Model

^{2}). The coefficient of determination (R

^{2}) measures the extent to which a model can accurately predict outcomes [69]. Advancements in AI modeling methodologies have facilitated enhanced accuracy in predicting anisotropic and amorphous material’s mechanical characteristics. This study employs a statistical error criterion to compare the ANN, KNN, and LR models.

^{2}holds promise for assessing the validity and precision of the model. Inadequate outcomes are observed when R

^{2}values fall below 0.50, while outcomes within the range of 0.65 to 0.75 for R

^{2}values are considered promising. The utilization of Equation (2) enables the determination of R

^{2}. The MAE is a metric used to determine the mean errors in the estimated values, irrespective of their direction. The input and output units of the MAE are identical. Despite the fact that a model’s MAE may fall within a specific tolerance range, it still can occasionally produce significant errors. The calculation of MAE is derived from Equation (3). The RMSE represents the average of the squared differences between estimated values and actual measurements. The calculation of the error square involves the summation of all the squared errors. This novel methodology assigns a higher degree of importance to exceptional instances than previous computations, resulting in substantial disparities squared in specific scenarios while yielding relatively lower disparities in others. The RMSE can be computed to ascertain the model’s average computational discrepancy when provided with an input. Enhanced models have a reduced RMSE. The RMSE is a metric used to evaluate the analytical accuracy of a model concerning future data. The RMSE is calculated by Equation (4). An estimator’s MSE or mean squared deviation quantifies the average of the squared errors, representing the average squared discrepancy between the estimated values and the true value. The calculation of the MSE involves the utilization of Equation (5).

#### 2.4. RreliefF Analysis

## 3. Results

#### 3.1. ANN Model Results

^{2}value of 0.87. Furthermore, Figure 7b illustrates the error probability for percentage loss in CS. The error dispersion of ANN reveals that the mean error for the test set is 1.32%. The errors varied from a minimum of 0.02% to a maximum of 5.55%, with a standard deviation of 0.95%, indicating a relatively wide spread of values. Moreover, the error analysis indicates that only 5.6% of the absolute errors exceed 3%, whereas approximately 50% of observation errors were between 1 and 3%. However, a significant proportion of the 44.4% error was computed to be less than 1%.

#### 3.2. KNN Model Results

^{2}value of 0.81. Figure 8b visually depicts the error scattering of the KNN model between the test group and the predicted group. The model has a mean error of about 1.57%, with a maximum error of approximately 6.34% and a minimum error of approximately 0.01%. It is concluded by error analysis that approximately 12.8% of computed errors lie above 3%, whereas 53.4% of the errors lie within the 1 to 3% range. However, approximately 33.8% proportions of the errors were below 1%.

#### 3.3. LR Model Results

^{2}= 0.78 with the projected output values, as depicted in Figure 9a. Additionally, Figure 9b illustrates the scattering of errors associated with the model. The provided illustration showcases the range of inaccuracies, revealing that the average error is approximately 1.69%. Furthermore, the analysis of error concentration reveals that the standard deviation of the error is 1.24%, with an extreme error of 6.74% and the lowest error of 0.00%. The error analysis revealed that approximately 14.2% of the absolute errors exceed 3%, whereas 51.7% of observation errors were within the range of 1 to 3%. Furthermore, approximately 34.2% of absolute errors were under 1%.

#### 3.4. Validation Results

^{2}(0.87) and error rates compared to the LR and KNN machine learning methods.

#### 3.5. Results of RreliefF Analysis

## 4. Discussions

^{2}scores and lesser RMSE and MAE values. The result can be compared with the recent literature tabulated in Table 5. The observed outcome might be attributed to ANN’s enhanced robustness compared to other models, such as KNN and LR. The application of ANN enables a precise assessment of the fraction loss in CS of ESP and RGP-based modified composites by effectively capturing the intricate relationships among many factors, including ESP and RGP concentration, water-cement ratio, and age. ANN employs a system of interconnected neurons to acquire knowledge and make predictions, relying on the observed correlation between these variables. During the learning phase, the ANN model incorporates the entire training dataset as an additional precautionary measure for enhanced security. By considering all of these aspects, ANN is capable of generating dependable predictions while disregarding any extraneous data or noise that could undermine the accuracy of its outcomes. Consequently, only the properties that hold significant importance in determining the resilience of ESP and RGP-based composites are considered during the prediction phase. The findings of this analysis indicate that the KNN and LR models exhibit limited reliability in their predictive capabilities. Acid-attack tests encompass multiple components and interactions that may not correspond to conventional linear or nearest-neighbor correlations. KNN relies on the close proximity of data points in the feature space [61]; however, it may not accurately capture the nonlinear relationships that exist in acid-attack data. Due to its linear nature, LR may have difficulties in accurately representing the complex relationships [79] associated with the decrease in CS after acid attack. KNN and LR algorithms necessitate manual feature engineering or encounter difficulties in managing feature spaces with a high number of dimensions, which limits the reliability of these models.

^{2}values for ANN, KNN, and LR are 213.99, 167.47, and 140.08, respectively, suggesting a significant deviation from the independence assumption. In this visual representation, the relative size of each rectangle corresponds to the expected percentage loss in CS. In contrast, the observed percentage loss in CS is depicted by the number of squares within each rectangle. Therefore, the distinction between the experimental and estimated values is visually represented by the intensity of shading, with color utilized to signify whether the departure from independence is positive or negative. In this graph, solid lines with blue color are utilized to represent positive deviations, while red lines are employed to depict negative deviations. As seen in Figure 12 below, the blue shad in KNN and LR is much greater than ANN, which suggests that ANN outperformed other ML techniques.

## 5. Conclusions

- A strong correlation was seen between the developed ML models and the results obtained from testing, signifying their probable applicability in estimating the fraction loss in CS of cement-based composites treated with ESP and RGP.
- The ANN model was considered more favorable than the KNN and LR models because of its higher accuracy level, as evidenced by the R
^{2}values (0.87 for ANN, 0.81 for KNN, and 0.78 for LR). - The evaluation of errors, such as MSE, MAE, and RMSE, suggested that the ANN model prediction capabilities were more substantial than the KNN and LR models. The ANN model exhibits MSE, MAE, and RMSE values of 6.51%, 1.32%, and 1.63%, respectively.
- The feature importance graph suggested that the concentration of ESP and RGP, as well as the percentage of cement, greatly influenced the decline in CS subjected to acid-attack tests with the RreliefF scores of 0.26, 0.215, and 0.169, respectively.
- The sieve diagram also indicated that the ANN model outperformed other ML methods (KNN and LR), with the χ
^{2}value of 213.99, suggesting that the two variables were significantly related.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Converting eggshell waste to treated eggshell powder [16].

**Figure 3.**Frequency histograms (

**a**) cement, (

**b**) sand, (

**c**) water, (

**d**) silica fume, (

**e**) superplasticizer, (

**f**) glass powder, (

**g**) eggshell powder, and (

**h**) 90 days of CS [48].

**Figure 5.**Structure of ANN obtained from [60].

**Table 1.**Descriptive statistics of input variables [48].

Parameters | Cement | Sand | Water | Silica Fume | Superplasticizer | Glass Powder | Eggshell Powder | 90-Day CS |
---|---|---|---|---|---|---|---|---|

Units | kg/m^{3} | kg/m^{3} | kg/m^{3} | kg/m^{3} | kg/m^{3} | kg/m^{3} | kg/m^{3} | MPa |

Mean | 732.51 | 735.62 | 191.33 | 151.67 | 38.17 | 30.83 | 30.83 | 46.56 |

Median | 722.00 | 729.00 | 191.00 | 153.00 | 38.00 | 0.00 | 0.00 | 45.17 |

Mode | 810.00 | 810.00 | 203.00 | 122.00 | 40.50 | 0.00 | 0.00 | 41.93 |

Standard Deviation | 53.51 | 54.39 | 9.41 | 23.75 | 1.84 | 40.15 | 40.15 | 7.02 |

Kurtosis | −0.61 | −0.61 | −1.51 | −1.51 | −1.51 | −0.61 | −0.61 | 0.22 |

Skewness | −0.24 | −0.32 | 0.05 | −0.08 | 0.14 | 0.93 | 0.93 | 0.70 |

Minimum | 612.00 | 612.00 | 180.00 | 122.00 | 36.00 | 0.00 | 0.00 | 32.32 |

Maximum | 810.00 | 810.00 | 203.00 | 180.00 | 40.50 | 121.50 | 121.50 | 66.92 |

Count | 234.00 | 234.00 | 234.00 | 234.00 | 234.00 | 234.00 | 234.00 | 234.00 |

**Table 2.**Parameters utilized in constructing the ANN model (parameters similar to [61]).

Parameter | Assigned Function |
---|---|

Neurons in hidden layers | 500 |

Activation | ReLu |

Solver | SGD |

Regularization (α) | 0.0001 |

Maximal no of iteration | 1000 |

Replicable training | yes |

Parameter | Assigned Function |
---|---|

No of neighbors | 8 |

Distance matrix | Chebyshev |

Weight | Uniform |

Errors (%) | ANN | KNN | LR |
---|---|---|---|

R^{2} | 0.87 | 0.81 | 0.78 |

MSE | 5.61 | 7.17 | 9.39 |

RMSE | 1.63 | 1.98 | 2.07 |

MAE | 1.32 | 1.57 | 1.69 |

Materials | Properties | Models | Outperformed Model | Reference |
---|---|---|---|---|

Self-healing concrete | CS | ANN, ANFIS | ANN | [80] |

Alkali-activated materials | CS | KNN, ANN, DT | ANN | [61] |

GGBFS-based concrete | CS | LR, ANN, non-LR, quadratic, full quadratic models | ANN | [81] |

Concrete with hooked steel fibers | CS | KNN, ANN | ANN | [82] |

Concrete bricks utilizing various industrial wastes like fly ash, rise husk ash, and hydrated lime | Water absorption, CS, Density | Multiple LR, ANN | ANN | [83] |

RGP-based concrete | CS | ANN, LR, non-LR | ANN | [79] |

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## Share and Cite

**MDPI and ACS Style**

Zhu, F.; Wu, X.; Lu, Y.; Huang, J.
Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study. *Buildings* **2024**, *14*, 225.
https://doi.org/10.3390/buildings14010225

**AMA Style**

Zhu F, Wu X, Lu Y, Huang J.
Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study. *Buildings*. 2024; 14(1):225.
https://doi.org/10.3390/buildings14010225

**Chicago/Turabian Style**

Zhu, Fei, Xiangping Wu, Yijun Lu, and Jiandong Huang.
2024. "Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study" *Buildings* 14, no. 1: 225.
https://doi.org/10.3390/buildings14010225