# Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

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## Abstract

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## 1. Introduction

## 2. Methods and Datasets

#### 2.1. Multi Expression Programming

#### 2.2. Modeling Dataset

#### 2.3. Cross-Validation with k-Fold Algorithm

#### 2.4. Performance Measures

## 3. Experimental Methods

#### 3.1. Sugarcane Bagasse Ash (SCBA) Characterization

#### 3.2. Mix Proportions and Specimen Preparation

## 4. Results and Discussion

#### 4.1. Mechanical Properties of SCBA Concrete

#### 4.2. Modeling Results of SCBA Concrete

#### 4.2.1. Formulation of Compressive Strength (CS)

#### 4.2.2. Formulation of Splitting Tensile Strength (ST)

#### 4.2.3. Formulation of Flexural Strength (FS)

#### 4.3. Models Error Assessment

#### 4.4. Model Cross-Validation Results

#### 4.5. Parametric Analysis

## 5. Conclusions

- The SCBA showed good pozzolanic properties when processed, i.e., passed from sieve #200 and grinded up to cement fineness.
- Microfibrous structure and irregular shape particles were observed in the SEM images of processed SCBA.
- The concrete showed maximum strength when cement was replaced with 10% SCBA. Afterward, strength reduction was observed for higher replacement levels.
- The multi expression programming (MEP) was found to be very efficient in modeling the strength properties of SCBA concrete. The parametric study showed that the developed MEP models for SCBA concrete are accurate and revealed the effect of input parameters in the modeling output.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Frequency histograms of the input data (

**a**) w/c (

**b**) sugarcane bagasse ash (SCBA)% (

**c**) CC (

**d**) FA (

**e**) CA.

**Figure 9.**Description of the absolute error among model predicted and actual data for (

**a**) CS (

**b**) ST (

**c**) FS.

Parameters | Setting |
---|---|

Number of subpopulations | 50 |

Size of subpopulation | 250 |

Code length | 40 |

Crossover probability | 0.9 |

Mathematical operators | +, −, ×, ÷ |

Mutation probability | 0.01 |

Tournament size | 4 |

Operators | 0.5 |

Variables | 0.5 |

Number of generations | 1000 |

Parameter | W/C | CC | SCBA% | FA | CA |
---|---|---|---|---|---|

Unit | – | Kg/m^{3} | % | Kg/m^{3} | Kg/m^{3} |

Range | 0.3 | 444 | 50 | 614 | 772 |

Min | 0.3 | 112 | 0 | 239 | 477 |

Max | 0.6 | 555 | 50 | 853 | 1249 |

Mean | 0.47 | 336.5 | 13.98 | 603.5 | 884.6 |

SD | 0.074 | 98.5 | 10.46 | 232.1 | 392.3 |

Composition | Percentage |
---|---|

SiO_{2} | 66.28 |

Al_{2}O_{3} | 8.36 |

Fe_{2}O_{3} | 1.39 |

CaO | 9.06 |

MgO | 5.56 |

P_{2}O_{5} | 2.46 |

K_{2}O | 3.52 |

Na_{2}O | 1.30 |

TiO_{2} | 0.19 |

MnO | 0.02 |

LOI | 1.67 |

Moisture content | 1.15 |

Mix | Cement Kg/m^{3} | CA Kg/m ^{3} | SCBA Kg/m ^{3} | W/C | FA Kg/m ^{3} | Water Kg/m ^{3} | Density (Kg/m^{3}) | |||
---|---|---|---|---|---|---|---|---|---|---|

Cement | CA | FA | SCBA | |||||||

CM | 366 | 1013.5 | 0 | 0.5 | 742.3 | 183 | 3150 | 2510 | 1680 | 2450 |

10BC | 329.4 | 1013.5 | 36.6 | 0.5 | 742.3 | 183 | ||||

20BC | 292.8 | 1013.5 | 73.2 | 0.5 | 742.3 | 183 | ||||

30BC | 256.2 | 1013.5 | 109.8 | 0.5 | 742.3 | 183 | ||||

40BC | 219.6 | 1013.5 | 146.4 | 0.5 | 742.3 | 183 |

Mix | Compressive Strength (MPa) | ||||
---|---|---|---|---|---|

0BC | 10BC | 20BC | 30BC | 40BC | |

Sample 1 | 23.5 | 23.9 | 21.5 | 18.5 | 16.7 |

Sample 2 | 22.7 | 23.6 | 21.6 | 19.6 | 15.6 |

Sample 3 | 22.9 | 23.7 | 21.2 | 19.1 | 16.4 |

Sample 4 | 23.4 | 24.2 | 22.3 | 19.5 | 15.7 |

Average | 23.1 | 23.8 | 21.6 | 19.1 | 16.1 |

Splitting Tensile Strength (MPa) | |||||

Sample 1 | 6.3 | 7.9 | 7.2 | 6.7 | 5.3 |

Sample 2 | 6.2 | 7.8 | 7.3 | 5.6 | 4.7 |

Sample 3 | 6.2 | 8.1 | 7.5 | 5.3 | 4.4 |

Sample 4 | 6.7 | 8.1 | 7.5 | 5.8 | 4.9 |

Average | 6.3 | 7.9 | 7.3 | 5.8 | 4.8 |

Flexural Strength (MPa) | |||||

Sample 1 | 4.7 | 5.1 | 3.9 | 3.1 | 2.8 |

Sample 2 | 4.3 | 5.1 | 3.8 | 3.3 | 2.6 |

Sample 3 | 4.6 | 5.2 | 3.8 | 3.3 | 2.6 |

Sample 4 | 4.6 | 5.3 | 3.7 | 3.2 | 2.5 |

Average | 4.5 | 5.1 | 3.8 | 3.2 | 2.6 |

Models | Data | NSE | R | RMSE | MAE | RSE | RRMSE | ρ |
---|---|---|---|---|---|---|---|---|

CS | Training | 0.87 | 0.91 | 3.47 | 2.96 | 0.16 | 0.04 | 0.020 |

Testing | 0.89 | 0.94 | 2.98 | 2.98 | 0.12 | 0.09 | 0.046 | |

Validation | 0.89 | 0.93 | 2.87 | 1.67 | 0.15 | 0.04 | 0.020 | |

ST | Training | 0.85 | 0.90 | 2.43 | 3.67 | 0.23 | 0.09 | 0.047 |

Testing | 0.91 | 0.92 | 2.65 | 3.69 | 0.26 | 0.12 | 0.062 | |

Validation | 0.90 | 0.92 | 3.25 | 3.98 | 0.31 | 0.10 | 0.052 | |

FS | Training | 0.86 | 0.91 | 3.92 | 1.87 | 0.29 | 0.13 | 0.068 |

Testing | 0.87 | 0.91 | 3.34 | 1.45 | 0.28 | 0.15 | 0.078 | |

Validation | 0.86 | 0.93 | 3.67 | 2.87 | 0.19 | 0.16 | 0.079 |

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## Share and Cite

**MDPI and ACS Style**

Shah, M.I.; Amin, M.N.; Khan, K.; Niazi, M.S.K.; Aslam, F.; Alyousef, R.; Javed, M.F.; Mosavi, A.
Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete. *Sustainability* **2021**, *13*, 2867.
https://doi.org/10.3390/su13052867

**AMA Style**

Shah MI, Amin MN, Khan K, Niazi MSK, Aslam F, Alyousef R, Javed MF, Mosavi A.
Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete. *Sustainability*. 2021; 13(5):2867.
https://doi.org/10.3390/su13052867

**Chicago/Turabian Style**

Shah, Muhammad Izhar, Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Sohaib Khan Niazi, Fahid Aslam, Rayed Alyousef, Muhammad Faisal Javed, and Amir Mosavi.
2021. "Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete" *Sustainability* 13, no. 5: 2867.
https://doi.org/10.3390/su13052867