# Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer

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## Abstract

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_{pc}) and cylindrical compressive strength (σ

_{cc}) by 113% to 577% and 53% to 459%, depending on mix proportion and curing period. An amorphous gel fills the porous places between the cement particles were formed when the CG was treated with water reducer polymer, which reduces voids, increases porosity, and increases the cement’s dry density; as a result, the CS of the CG increases significantly. To evaluate the CS of CG with different grain sizes, w/c, percentage of polymer, and curing age, linear and nonlinear techniques were used. according to the bs standard, the CS of the CG produced was 71% higher than that of the identical mix produced according to the ASTM standard. Compared to the other sands, the cement grout produced with finer sand grading had the maximum flexural strength at all testing ages.

## 1. Introduction

_{f}. Several parameters, including cement to sand ratios, w/c, raw materials utilized, sand grading, and sand particle morphology, influence the strength properties of CM [9,10].

- (i).
- Assess the effect of different water reducer polymer additive surfaces on CG characteristics using scanning electron microscopy testing (SEM).
- (ii).
- Investigate the effect of five grains of sand with various sizes on the flowability of CG modified with water reducer polymer.
- (iii).
- Calculate the maximum compression and flexural stress of self-compacting CG using linear and nonlinear techniques.
- (iv).
- Correlating prismatic compressive strength to cylindrical compressive strength followed by the American Society for Testing and Materials and British Standards, and prismatic compressive strength to the flexural strength of a CG.

## 2. Materials and Methods

#### 2.1. Materials

#### 2.1.1. Polymer

#### 2.1.2. Cement

#### 2.1.3. Sands

#### 2.2. Methods

#### 2.2.1. XRD Analysis

#### 2.2.2. Scanning Electron Microscopy (SEM)

#### 2.2.3. Mix Proportions

#### 2.2.4. Flow

#### 2.2.5. Compressive Strength

#### 2.3. Data Analysis

#### 2.3.1. Linear Model

_{pc}= α

_{1}+ α

_{2}∗ w/c + α

_{3}∗ t + α

_{4}∗ d

_{10}+ α

_{5}∗ P

_{cc}= α

_{6}+ α

_{7}∗ w/c + α

_{8}∗ t + α

_{9}∗ d

_{10}+ α

_{10}∗ P

_{f}= α

_{11}+ α

_{12}∗ w/c + α

_{13}∗ t + α

_{14}∗ d

_{10}+ α

_{15}∗ P

#### 2.3.2. Nonlinear Model

_{pc}, σ

_{cc,}and σ

_{f}, including the w/c and polymer dosage, d

_{10}, and testing age (t), is also evaluated using Equations (4)–(6) [17,18,19,20,21,22,23,24].

## 3. Result and Analysis

#### 3.1. Selection of w/c

#### 3.2. Flow and w/c

#### 3.3. Microstructure Tests

#### 3.4. Stress at Failure

#### 3.4.1. ASTM Standard

_{10}, according to the model parameters in Equations (7) and (8). The NLR model predicted the σ

_{cc}stronger than the LR model, as shown in Figure 9a,b. The residual error was ranged between +12 to −16 MPa (Figure 9a,b).

#### 3.4.2. BS Standard

#### 3.4.3. ASTM Standard and BS Standard conversion factor

^{2}of 0.95 and RMSE of 2.8 MPa (Figure 12).

_{cc}= 0.71 ∗ σ

_{cp},

_{f}at all testing ages.

_{f}of cement grout (Figure 14a,b).

## 4. Limitations and Recommendations for Future Works

## 5. Conclusions

- Several conclusions can be taken depending on the modeling and tested results.
- At a lower water/cement ratio, cement-basedgrout used with coarse-grained sand had higher compression strength than fine-grained sand. At a high water/cement ratio, cement-based grout prepared with fine-grained sand had higher strengths efficiency than coarse-grained sand.
- In parallel with the addition of water reducer polymer, a reduction by 21.9% to 54.1 % was observed in the water/cement ratio depending on the proportion of polymers, and the cement grout flow was maintained between 18 and 23 s.
- According to the electronic microscopy scanning test, the cement particles were coated by a mesh fiber created by the water reducer polymer, causing a decrease in porosity and an increase in density, increasing the cement grout’s compressive strength.
- According to the linear and nonlinear model parameters, the polymer has the greatest impact on improving the compression strength of cement grout compared to the other mix proportions.
- According to statistical analyses, the nonlinear technique performed better than the linear technique for predicting the cement-grouted sand compressive strength.
- The cement grout compression strength was determined using a cylinder and prismatic molds following ASTM and BS standards. The conversion factor was 0.71.
- In comparison to the other sands, the cement grout prepared with finer sand had the maximum flexural strength at all testing ages.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Shiotani, T.; Momoki, S.; Chai, H.; Aggelis, D.G. Elastic wave validation of large concrete structures repaired by means of cement grouting. Constr. Build. Mater.
**2009**, 23, 2647–2652. [Google Scholar] [CrossRef] [Green Version] - Faramarzi, L.; Rasti, A.; Abtahi, S.M. An experimental study of the effect of cement and chemical grouting on the improvement of the mechanical and hydraulic properties of alluvial formations. Constr. Build. Mater.
**2016**, 126, 32–43. [Google Scholar] [CrossRef] - Mohammed, M.H.; Pusch, R.; Knutsson, S.; Hellstr, G. Rheological properties of cement-based grouts determined by different techniques. Engineering
**2014**, 6, 217–229. [Google Scholar] [CrossRef] [Green Version] - Du, X.; Fang, H.; Wang, S.; Xue, B.; Wang, F. Experimental and practical investigation of the sealing efficiency of cement grouting in tortuous fractures with flowing water. Tunn. Undergr. Space Technol.
**2021**, 108, 103693. [Google Scholar] [CrossRef] - Cao, J.; Gao, J.; Rad, H.N.; Mohammed, A.S.; Hasanipanah, M.; Zhou, J. A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young’s modulus and unconfined compressive strength of rock. Eng. Comput.
**2021**, 1–17. [Google Scholar] [CrossRef] - Mohammed, A.; Burhan, L.; Ghafor, K.; Sarwar, W.; Mahmood, W. Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers. Neural Comput. Appl.
**2021**, 33, 7851–7873. [Google Scholar] [CrossRef] - Burhan, L.; Ghafor, K.; Mohammed, A. Testing and evaluation of flowability, viscosity and long-term compressive strength of cement modified with polymeric admixture WR superplasticizer. IOP Conf. Ser. Mater. Sci. Eng.
**2020**, 737, 012066. [Google Scholar] [CrossRef] - Mohammed, A.; Mahmood, W.; Ghafor, K. Shear stress limit, rheological properties and compressive strength of cement-based grout modified with polymers. J. Build. Pathol. Rehabil.
**2020**, 5, 3. [Google Scholar] [CrossRef] - Emad, W.; Salih, A.; Kurda, R.; Hassan, A.M.T. Multivariable models to forecast the mechanical properties of polymerized cement paste. J. Mater. Res. Technol.
**2021**, 14, 2677–2699. [Google Scholar] [CrossRef] - Thanaraj, M.S. Investigations on Improving the Compressive Strength of Sand Column with Cement Grout and Chemical Admixture. Turk. J. Comput. Math. Educ.
**2021**, 12, 1841–1847. [Google Scholar] - Mahmood, W.; Mohammed, A.S.; Sihag, P.; Asteris, P.G.; Ahmed, H. Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches. Arch. Civil Mech. Eng.
**2022**, 22, 1–25. [Google Scholar] [CrossRef] - Sarwar, W.; Ghafor, K.; Mohammed, A. Regression analysis and Vipulanandan model to quantify the effect of polymers on the plastic and hardened properties with the tensile bonding strength of the cement mortar. Results Mater.
**2019**, 1, 100011. [Google Scholar] [CrossRef] - Avci, E.; Deveci, E.; Gokce, A. Effect of Sodium Silicate on the Strength and Permeability Properties of Ultrafine Cement Grouted Sands. J. Mater. Civ. Eng.
**2021**, 33, 04021203. [Google Scholar] [CrossRef] - Cai, M.; Hocine, O.; Mohammed, A.S.; Chen, X.; Amar, M.N.; Hasanipanah, M. Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential. Eng. Comput.
**2021**, 1–13. [Google Scholar] [CrossRef] - Murlidhar, B.R.; Bejarbaneh, B.Y.; Armaghani, D.J.; Mohammed, A.S.; Mohamad, E.T. Application of tree-based predictive models to forecast air overpressure induced by mine blasting. Nat. Resour. Res.
**2021**, 30, 1865–1887. [Google Scholar] [CrossRef] - Yu, C.; Koopialipoor, M.; Murlidhar, B.R.; Mohammed, A.S.; Armaghani, D.J.; Mohamad, E.T.; Wang, Z. Optimal ELM–Harris Hawks optimization and ELM–Grasshopper optimization models to forecast peak particle velocity resulting from mine blasting. Nat. Resour. Res.
**2021**, 30, 2647–2662. [Google Scholar] [CrossRef] - Zeng, J.; Asteris, P.G.; Mamou, A.P.; Mohammed, A.S.; Golias, E.A.; Armaghani, D.J.; Faizi, K.; Hasanipanah, M. The effectiveness of ensemble-neural network techniques to predict peak uplift resistance of buried pipes in reinforced sand. Appl. Sci.
**2021**, 11, 908. [Google Scholar] [CrossRef] - Huang, J.; Asteris, P.G.; Pasha, S.M.K.; Mohammed, A.S.; Hasanipanah, M. A new auto-tuning model for predicting the rock fragmentation: A cat swarm optimization algorithm. Eng. Comput.
**2020**, 1–12. [Google Scholar] [CrossRef] - Vipulanandan, C.; Ali, M.; Basirat, B.; Reddy, A.; Amin, N.; Mohammed, A.; Dighe, S.; Farzam, H. Field test for real time monitoring of piezoresistive smart cement to verify the cementing operations. In Proceedings of the Offshore Technology Conference, Houston, TX, USA, 2–5 May 2016; OnePetro, 2016. [Google Scholar]
- Avci, E. The effect of different curing temperatures on the strength of microfine cement grouted sands. Rom. J. Mater.
**2021**, 51, 272–280. [Google Scholar] - Emad, W.; Salih, A.; Kurda, R. Forecasting the mechanical properties of soilcrete using various simulation approaches. In Structures; Elsevier: Amsterdam, The Netherlands, 2021; Volume 34, pp. 653–665. [Google Scholar]
- Puertas, F.; Santos, H.; Palacios, M.; Martínez-Ramírez, S. Polycarboxylate superplasticiser admixtures: Effect on hydration, microstructure and rheological behaviour in cement pastes. Adv. Cem. Res.
**2005**, 17, 77–89. [Google Scholar] [CrossRef] - Hamad, A.J. Size and shape effect of specimen on the compressive strength of HPLWFC reinforced with glass fibres. J. King Saud Univ. Eng. Sci.
**2017**, 29, 373–380. [Google Scholar] [CrossRef] [Green Version] - Malaikah, A.S. Effect of specimen size and shape on the compressive strength of high strength concrete. Pertanika J. Sci. Technol.
**2005**, 13, 87–96. [Google Scholar]

**Figure 8.**Typical cylindrical CS of CG modified with water reducer polymer at (

**a**) one day of curing and (

**b**) 28 days of curing.

**Figure 9.**Measured and predicted relationship for the cylindrical CS of CG (

**a**) LR model, and (

**b**) NLR model.

**Figure 10.**Typical variation of prismatic CS of CG modified with water reducer polymer-polymer at (

**a**) one d and (

**b**) 28 d of curing.

**Figure 11.**Measured and predicted relationship for the Prismatic CS of CG (

**a**) LR model, (

**b**) NLR model, and (

**c**) residual error.

**Figure 12.**Prismatic and cylindrical CS correlation of CG modified with water reducer polymer 3.5. Flexural Strength.

**Figure 13.**Typical variation of σ

_{f}for CG modified with water reducer polymer at (

**a**) 1 day of curing, and (

**b**) 28 days of curing.

**Figure 14.**Measured and predicted relationship for the flexural strength of CG (

**a**) LR model, (

**b**) NLR model, and (

**c**) residual error.

**Figure 15.**Correlation between compressive and flexural strengths of CG modified with water reducer polymer.

Sand No. | d_{10} (mm) | d_{30} (mm) | d_{50} (mm) | Gs |
---|---|---|---|---|

1 | 0.42 | 0.62 | 0.75 | 2.62 |

2 | 0.18 | 0.22 | 0.29 | 2.59 |

3 | 0.33 | 0.42 | 0.51 | 2.65 |

4 | 0.14 | 0.28 | 0.41 | 2.64 |

5 | 0.31 | 0.36 | 0.42 | 2.66 |

Sand | Water/Cement Ratio | Flow Time (s) | Axial Strength (7 Days) (MPa) | Compressive Strength (28 Days) (MPa) |
---|---|---|---|---|

1 | 0.5 | 31.2 | 31.2 | 37.2 |

0.53 | 26.5 | 28.1 | 34.5 | |

0.57 | 24.2 | 16.1 | 22.2 | |

0.6 | 18.7 | 16.8 | 20.8 | |

2 | 0.5 | 30.6 | 25.4 | 32.7 |

0.53 | 28.1 | 23 | 29.6 | |

0.57 | 27.3 | 20.4 | 26.2 | |

0.6 | 19.3 | 15.2 | 20.6 | |

3 | 0.5 | 29.2 | 29 | 36.8 |

0.53 | 27.0 | 28 | 32.7 | |

0.57 | 23.6 | 16 | 21.8 | |

0.6 | 19.3 | 15.2 | 20.6 | |

4 | 0.5 | 30.1 | 25.3 | 33.8 |

0.53 | 27.8 | 24.6 | 31.1 | |

0.57 | 25.4 | 19.2 | 24.9 | |

0.6 | 21.0 | 17.3 | 22.5 | |

5 | 0.5 | 28.22 | 28.9 | 34.5 |

0.53 | 27.3 | 27.4 | 31.9 | |

0.57 | 23.8 | 17.8 | 23.3 | |

0.6 | 20.2 | 16.2 | 21.6 |

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**MDPI and ACS Style**

Mahmood, W.; Mohammed, A.S.; Asteris, P.G.; Kurda, R.; Armaghani, D.J.
Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer. *Appl. Sci.* **2022**, *12*, 1016.
https://doi.org/10.3390/app12031016

**AMA Style**

Mahmood W, Mohammed AS, Asteris PG, Kurda R, Armaghani DJ.
Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer. *Applied Sciences*. 2022; 12(3):1016.
https://doi.org/10.3390/app12031016

**Chicago/Turabian Style**

Mahmood, Wael, Ahmed Salih Mohammed, Panagiotis G. Asteris, Rawaz Kurda, and Danial Jahed Armaghani.
2022. "Modeling Flexural and Compressive Strengths Behaviour of Cement-Grouted Sands Modified with Water Reducer Polymer" *Applied Sciences* 12, no. 3: 1016.
https://doi.org/10.3390/app12031016