# Systematic Evaluation of Permeability of Concrete Incorporating Coconut Shell as Replacement of Fine Aggregate

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{−11}m and 5%, respectively. In the second scenario, an acceptable and reasonable low permeability (less than 2.7 × 10

^{−11}m/s) and water absorption (less than 6.7%) were also obtained when the replacement percentage increased up to 60%. In contrast, the high content coconut shell, such as 90% and 100%, developed concrete with a high permeability and water absorption and was defined in the third scenario. It was also inferred that both the experimental and mathematical models (ANN, GEP, and RSM) have consistent and accurate results. The correlation statistics indicators (R

^{2}) were greater than 0.94 and the error was less than 0.3, indicating a strong correlation and minimum error. In conclusion, coconut shell could act as a good alternative material to produce cleaner concrete with an optimum value of 50% as a fine aggregate replacement.

## 1. Introduction

^{3}) in a study reported by El Mir and Nehme [27]. Similarly, exploiting polished granite waste as a partial replacement of coarse aggregate exhibited a better result for both water permeability and water absorption [28,29]. In contrast, the water permeability of concrete incorporating waste glass as a partial replacement of sand increased owing to the development of extra voids between cement paste and waste glass particles at the interface [30]. In the same context, the combination of both coconut shell and fly ash as partial replacements of coarse aggregate and cement, respectively, improved the resistance against water absorption and permeability in a study reported by Prakash and Thenmozhi [31]. Similarly, the replacement of coarse aggregate by coconut shell in self-compacting concrete exhibited positive results up to 75% in the presence of silica fume and rice husk ash [32,33,34].

## 2. Experimental and Informational Modeling

#### 2.1. Experiment Design

^{n}factorial points where n is the number of independent variables. Figure 2 shows the required experimental tests on the basis that two independent variables were used. It is interesting to note that the coded values +1 and −1 refer to the high and low limit of each parametrize, while α is the distance from center of the cube which equal to 1.414 in the present study. It should be also noted that five center points were adopted to examine and assess the prediction error. Similarly, the number of experimental tests (Q) was found to be thirteen using Equation (1) where m represented the number of center points [45]. Moreover, Equation (2) was used to convert the coded values to real values where Z and Zc are the real value of the independent value and real value of independent variables at the center point, respectively [46]. Furthermore, L denoted the coded value of the independent variable.

_{ii}is the quadratic coefficients, β

_{o}corresponds to intercept of the model, and β

_{i}denotes to the linear coefficients. Furthermore, X

_{1}and X

_{2}represent the input data involving CA content and time, while Y is the response (permeability and water absorption). For the purpose of verification of the proposed equation, analysis of variance (ANOVA) was taken into account. In particular, R

^{2}was calculated to evaluate the closeness between the response and real results as shown in Equation (4) where SS

_{T}represents the total sum of square error, SS

_{E}is the sum of square error based on the predicted results, and ${\overline{Y}}_{A}$ denote the mean value of actual value. In addition, Y

_{P}and Y

_{A}are the predicted and real values. In the same context, ${R}_{adj.}^{2}$ was also calculated to evaluate the effect of the number of independent variables on the correlation between the real and predicted results as shown in Equation (5) [48] where DF is the degree of freedom and SS

_{R}represents the sum square of differences error between the actual and predicted values. The predicted ${R}_{pred.}^{2}$ was also determined according to Equation (6) [49]. The differences between ${R}_{pred.}^{2}$ and ${R}_{adj.}^{2}$ should be less than 0.2 to ensure that the equation has the ability to predict for more data [50] where W refers to the estimated residual sum of square without the ith. Meanwhile, the signal-to-noise ratio was evaluated using the adequate precision (SN) as shown in Equation (7) in which its value should be greater than 4 [51] where σ

^{2}denote to the residual mean square. In addition, the p-value and F-value were also taken into account to validate the significance of the proposed equation. The achievement of the high F-value and p-value less than 0.005 led to the equation being considered as significant [52].

#### 2.2. Preparation of Concrete Mix Design

^{3}and 536 kg/m

^{3}, respectively. It is interesting to note that the CA was added as a partial replacement of fine aggregate according to the suggested array experiment design of CCD as shown in Table 1. Rheobuild 1100 superplasticising admixture (up to 1.2% by cement weight) was also used to acquire a slump in the range of 100–140 mm. Table 2 exhibits the mix proportion of the proposed concrete.

#### 2.3. Water Absorption Test

_{2}and W

_{1}represent the wet weight of the concrete cube and the dry weight of the concrete cube, respectively. It is also interesting to note that the correction factor (CF) was taken into account to tackle the sample length variation, which was in line with the study reported by Kwan and Ramli [53].

#### 2.4. Permeability Test

_{w}) were obtained to evaluate the performance of concrete incorporating CA as shown in Equation (9) where d is the depth of water penetration, T refers to time under pressure, and h represent the hydraulic head. In addition, the porosity (v) is the function of the area of the cubes (A), water density ($\rho $), depth of penetration (d), and the differences of mass sample (m) as shown in Equation (10). To implement the test, the concrete cubes in the dimension of (100 × 100 × 100) mm were tested after 28 days for each experiment run. After the test arrangement was pressurized with 5 bars for 3 days as shown in Figure 4b, the concrete cubes were split into half and the water penetration depth measured.

#### 2.5. Prediction Model Using ANN

^{2}were adopted as statistic validation indictors to verify the accuracy and strength of the proposed equation as shown in Equations (12) and (13), respectively.

#### 2.6. Prediction Model Using GEP

^{2}were calculated to assess the relationship between the real and predicted results as descried in Equations (14) and (15). The closer the R to one, the strength and closeness results could be achieved. For the error statistic indicators, four methods were taken into account involving the mean absolute error (MAE), mean root relative squared error (RRSE), relative absolute error (RAE), and root mean square error (RMSE) as show in Equations (14)–(19).

## 3. Result and Discussion

#### 3.1. Parametric Analysis

#### 3.1.1. Water Absorption

#### 3.1.2. Water Permeability

^{−11}m/s at 28 days, which is in a good agreement with the present literature. For example, Cuadrado-Rica and Sebaibi [62] concluded that the range of normal concrete permeability was 1.0 × 10

^{−11}m/s to 5.0 × 10

^{−11}m/s. In addition, according to ACI standard 301-89, a high-quality concrete is obtained when the water permeability is lower than 1.5 × 10

^{−11}m/s. In our study, the water permeability of concrete containing 10% of the coconut shell as a replacement of the fine aggregate was found to be 1.2 × 10

^{−11}m/s confirming that the concrete quality is still high when it met the specifications. In the same context, with the increase in the coconut shell up to 60%, the water permeability of the concrete slightly increased up to 2.7 × 10

^{−11}m/s, which also can be considered a low and reasonable permeability. This fact is in line with Amriou and Bencheikh [63], who defined a low water permeability of concrete as when its value was located in the range between 8 × 10

^{−12}m/s and 3.2 × 10

^{−11}m/s. This result was consistent with all the data sets obtained from the RSM, ANN, and GEP models as shown in Figure 6a. It can be seen that the permeability of concrete containing coconut shell up to 55% was lesser than 2 × 10

^{−11}m/s in indicating that the concrete quality is good. After that, the slop of water permeability increased with the increases in coconut shell content, specifically, 90% and 100%. This result is also presented using a counter plot as shown in Figure 6b–d in which the blue color reflects low permeability, whereas the yellow and red colors relate to high permeability. This mean that the zone of low permeability was located between 0 and 60% of coconut shell replacement, while the incorporation of high content coconut shell (more than 60%) would weaken the resistance of the concrete permeability. In addition, it might be attributed to the increment of pores and interconnectivity pores of concrete incorporating the high content of coconut shell.

#### 3.2. Informational Modeling Using RSM

^{−13}, indicating that the model was able to estimate accurate results. This fact is in line with Algaifi and Alqarni [65] who used RMSE to prove the adequacy of the predicted equation of bacterial concrete strength. Based on their outcomes, the RSME was 2.04, confirming that the predicted and actual results were close and accurate. In the same contest, R

^{2}proved the closeness and correlation between the predicted and actual results in which the value of R

^{2}was high. According to Huseien and Sam [66], a good correlation could be obtained when R

^{2}is greater than 0.7. Herein, the R

^{2}value of WA and WP were 0.9993 and 0.9995, thus highlighting that the predicted results were acceptable. In addition, the capability of these quadratic equations to accurately predict further data was also proved using ${R}_{predicted}^{2}$ and ${R}_{adj}^{2}$. In particular, it was found that the differences between the ${R}_{predicted}^{2}$ and ${R}_{adj}^{2}$ were less than 0.2. This fact is in good agreement with the existing literature. For example, Jitendra and Khed [67] developed an RSM model to predict and optimize the water absorption, chloride ions penetration, and compressive strength of concrete blocks containing foundry sand and fly ash as partial replacements of natural sand and cement, respectively. The outcome of their study revealed that the model was reliable and could be used for further prediction. This is because a reasonable difference (less than 0.2) between ${R}_{predicted}^{2}$ and ${R}_{adj}^{2}$ was achieved for all data sets.

^{3}. This fact is line with Khoshkenari and Shafigh [68] and Nowak and Rakoczy [69], who demonstrated that the density of lightweight concrete has a density in the range of 1440–1840 kg/m

^{3}, while the density of ordinary or normal concrete is in the range between 2240 and 2400 kg/m

^{3}. Herein, the concrete density is divided into two zones as shown in Figure 8d. The first zone represents the normal concrete that has a density greater than 2240 kg/m, while the second zone represent the lightweight concrete that has a lower a density.

#### 3.3. Informational Modeling Using GEP and AMM

_{o})). The structural tree was later converted into a mathematical expression using the Karva language.

^{2}values of 0.991 and 0 and 9878 for training and validation. This outcome was similar to the present findings. In particular, the value of R

^{2}of the proposed water permeability equation using GEP and ANN were 0.9519 and 0.9719 for training, while its value was higher than 0.967 for validation. These positive results are almost in line with the findings from the predicted equation of water absorption using ANN and GEP. In particular, the R

^{2}were higher than 0.856 for both training and validations. As such, it can be inferred that both GEP and ANN models showed its ability to predict with high correlation and minimum error.

## 4. Conclusions

- All mathematical models of RSM, ANN, and GEP proved their ability to evaluate the behavior of CA-based concrete, in which the predicted data and the actual data were consistent.
- Based on ANN, GEP, and RSM models, the replacement percentage of fine aggregate by coconut shell up to 50% produce a good quality concrete in which the permeability and water absorption were less than 2.7 × 10
^{−11}m/s and 5%, receptively. - The ANN, RSM, and GEP also revealed that the high replacement of fine aggregate by coconut shell produced a concrete with high permeability (greater than 4.5 × 10
^{−11}m/s) and high water absorption (greater than 10%).

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 5.**Evolution of water absorption of CA-based concrete: (

**a**) all models (

**b**), RSM (

**c**) GEP, and (

**d**) ANN model.

**Figure 6.**Evolution of water permeability of CA-based concrete: (

**a**) all models, (

**b**) RSM, (

**c**) GEP, and (

**d**) ANN model.

**Figure 7.**Significance of the involved influential parameters on: (

**a**) water absorption and (

**b**) permeability.

**Figure 8.**Optimization of coconut shell: (

**a**) desirability functions, (

**b**) predicted WA evolution, (

**c**) predicted WP evolution, and (

**d**) CA-based density evolution.

**Figure 9.**Structural tree of the predicated behavior of CA-based concrete: (

**a**) water absorption and (

**b**) water permeability.

Run NO. | Coded Value | Real Value | CCD Division | ||
---|---|---|---|---|---|

Replaced Coconut Shell (%) | Time (Days) | ||||

1 | −1 | −1 | 10 | 7 | Factorial points (2^{n}) |

2 | 1 | −1 | 100 | 7 | |

3 | −1 | 1 | 10 | 28 | |

4 | 1 | 1 | 100 | 28 | |

5 | 1 | 0 | 100 | 17 | Axial points (2n) |

6 | −1 | 0 | 10 | 17 | |

7 | 0 | −1 | 55 | 7 | |

8 | 0 | 1 | 55 | 28 | |

9 | 0 | 0 | 55 | 17 | Centre points |

10 | 0 | 0 | 55 | 17 | |

11 | 0 | 0 | 55 | 17 | |

12 | 0 | 0 | 55 | 17 | |

13 | 0 | 0 | 55 | 17 |

Name of Specimens | Percentage of Fine Coconut Shell | Permeability m/s | Water Absorption % | ||
---|---|---|---|---|---|

7 Days | 28 Days | 7 Days | 28 Days | ||

FCSC10 | 10% | 8.92 × 10^{−12} | 1.2 × 10^{−11} | 4.65 | 5.21 |

FCSC20 | 20% | 1.37 × 10^{−11} | 1.53 × 10^{−11} | 5.12 | 5.48 |

FCSC30 | 30% | 1.48 × 10^{−11} | 1.73 × 10^{−11} | 5.34 | 5.53 |

FCSC40 | 40% | 1.81 × 10^{−11} | 1.96 × 10^{−11} | 5.96 | 6.28 |

FCSC50 | 50% | 2.02 × 10^{−11} | 2.3 × 10^{−11} | 5.99 | 6.63 |

FCSC60 | 60% | 2.17 × 10^{−11} | 2.7 × 10^{−11} | 6.37 | 7.28 |

FCSC70 | 70% | 2.58 × 10^{−11} | 3.38 × 10^{−11} | 7.78 | 8.17 |

FCSC80 | 80% | 3.59 × 10^{−11} | 3.56 × 10^{−11} | 8.45 | 8.77 |

FCSC90 | 90% | 4.52 × 10^{−11} | 4.69 × 10^{−11} | 9.54 | 10.23 |

FCSC100 | 100% | 5.47 × 10^{−11} | 6.43 × 10^{−11} | 11.63 | 13.85 |

Item | Second Polynomial Equations and the Involved Statistics Parameters | ||||
---|---|---|---|---|---|

Water Permeability (WP) | R^{2} = 0.999 | ${R}_{adj}^{2}=$0.9964 | ${R}_{predicted}^{2}$ 0.9813 | Adeq. Precision 44.47 | RMSE 8.2 × 10 ^{−13} |

$WP=10E-12\left(0.219+0.24{d}_{0}+2.78{d}_{1}+1.63{d}_{0}{d}_{1}+0.12{d}_{0}^{2}+0.1{d}_{1}^{2}\right)$ | |||||

Water Absorption (WA) | R^{2} = 0.9987 | ${R}_{adj}^{2}=$0.9953 | ${R}_{predicted}^{2}$ 0.976 | Adeq. Precision 40.757 | RMSE 0.1492 |

$WA=6.21+3.88{d}_{0}+0.59{d}_{1}+0.42{d}_{0}{d}_{1}+2.27{d}_{0}^{2}+0.355{d}_{1}^{2}$ |

Model | Item | Mathematical Equation and Related Statistics Validation Parameters | ||||
---|---|---|---|---|---|---|

GEP | WA | Training | MAE = 0.817 | RMSE = 1.037 | R = 0.925 | R^{2} = 0.856 |

Validation | MAE = 0.848 | RMSE = 0.959 | R = 0.982 | R^{2} = 0.964 | ||

$WA=\frac{2\sqrt{{d}_{1}}+{d}_{0}+{c}_{1}}{-2{d}_{1}{d}_{0}^{-1}+{c}_{2}+{c}_{0}}$ | ||||||

WP | Training | MAE = 2.73 × 10^{−12} | RMSE = 3.2 × 10^{−12} | R = 0.9756 | R^{2} = 0.9519 | |

Validation | MAE = 2.6 × 10^{−12} | RMSE = 2.8 × 10^{−12} | R = 0.995 | R^{2} = 0.991 | ||

$WP={10}^{-12}\times \left(0.5\sqrt[3]{{d}_{1}^{2}+{d}_{1}-{d}_{0}}+0.5EXP\left(\sqrt[3]{{d}_{0}}\right)\right)$ | ||||||

ANN | WA | Training | MAE = 0.32 | RMSE = 0.4447 | R = 0.979 | R^{2} = 0.9598 |

Validation | MAE = 0.123 | RMSE = 0.221 | R = 0.983 | R^{2} = 0.967 | ||

$WA=10.16-5.15\mathrm{Tan}H\left(0.5\left(4.91-0.05{d}_{0}-0.019{d}_{1}\right)\right)$ | ||||||

WP | Training | MAE = 2.0 × 10^{−12} | RMSE = 2.4 × 10^{−12} | R = 0.985 | R^{2} = 0.9719 | |

Validation | MAE = 6 × 10^{−13} | RMSE = 7 × 10^{−13} | R = 0.99 | R^{2} = 0.9981 | ||

$WP={10}^{-12}\times \left(45.9-33.7\mathrm{Tan}H\left(0.5\left(4.91-0.05{d}_{0}-0.019{d}_{1}\right)\right)\right)$ |

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## Share and Cite

**MDPI and ACS Style**

Mhaya, A.M.; Algaifi, H.A.; Shahidan, S.; Zuki, S.S.M.; Azmi, M.A.M.; Ibrahim, M.H.W.; Huseien, G.F.
Systematic Evaluation of Permeability of Concrete Incorporating Coconut Shell as Replacement of Fine Aggregate. *Materials* **2022**, *15*, 7944.
https://doi.org/10.3390/ma15227944

**AMA Style**

Mhaya AM, Algaifi HA, Shahidan S, Zuki SSM, Azmi MAM, Ibrahim MHW, Huseien GF.
Systematic Evaluation of Permeability of Concrete Incorporating Coconut Shell as Replacement of Fine Aggregate. *Materials*. 2022; 15(22):7944.
https://doi.org/10.3390/ma15227944

**Chicago/Turabian Style**

Mhaya, Akram M., Hassan Amer Algaifi, Shahiron Shahidan, Sharifah Salwa Mohd Zuki, Mohamad Azim Mohammad Azmi, Mohd Haziman Wan Ibrahim, and Ghasan Fahim Huseien.
2022. "Systematic Evaluation of Permeability of Concrete Incorporating Coconut Shell as Replacement of Fine Aggregate" *Materials* 15, no. 22: 7944.
https://doi.org/10.3390/ma15227944