Meta-Analysis and Machine Learning Models to Optimize the Efficiency of Self-Healing Capacity of Cementitious Material
1.1. Mechanism of Autogenous Self-Healing of Concrete
1.2. Crack Width
1.4. Fibers and Polymers
1.5. Treatment Environment and Period of Exposure
1.6. Systematic Review and Meta-Analysis
1.7. Artificial Neural Network (ANN) Model
1.8. Study Objective
3. Results and Discussions
3.1. Meta-Data Analysis
3.1.1. Crack Size
3.1.2. Healing Period
3.1.3. Crystalline Admixtures
3.1.4. Healing Environment
3.1.5. Presence of Fibres
3.1.6. Meta-Data Analysis: Comparisons
3.2. Relative Importance
3.3. Optimization Charts
3.4. Artificial Neural Network
- The statistical analysis has been carried out using statistical tools, like effect size, the margin of error, and the diamond ratio. The meta-analysis shows that crack size, healing period and environment, as well as the presence of CA has the highest effect on the self-healing efficiency. On the other hand, presence of fibers and SCM improves the self-healing capacity in cementitious materials; however, the effect is not as significant as other parameters.
- Based on the optimization charts, this study can suggest some recommendations and highlight the expected research works that would contribute to fill the gap and provide more data to strengthen the confidence relating to self-healing/sealing parameters. A brief analysis is drawn below.
- The relative importance percentage of total cementitious material, crack width, period of healing, w/c ratio, and fiber content in determining the SHI are 32.7%, 30.5%, 20%,13%, and 4.1%, respectively. The results validate the conclusions made based on the meta-analysis.
- The analysis suggests that the crack greater than 300 microns have much less healing efficiency, even for longer times of treatment, whereas cracks smaller than 100 microns can heal completely within a shorter rime, down to one week. Furthermore, it is observed that the rate of healing decreases notably after a period of 14 to 28 days of treatment depending on the crack size, hence corroborating with the reaction mechanism of autogenous self-healing in concrete.
- The design chart of w/c ratio and cement content shows that SHI increases with cement content even with low w/c ratios. This is because the hydration reactions augment with the availability of un-hydrated cement. However, more data is required for low-cement concrete with high w/c ratio to obtain better trends in optimization charts.
- The analysis of supplementary cementitious material shows that there is a peak range for SCM content, around 40–60% of total cementitious materials, for optimum SHI. This is because the hydration reaction of fly-ash utilizes the hydration products of cement. The low amount of cement produces less hydration energy for the activation of the hydration reaction of fly-ash.
- The design chart investigating the effect of the period of healing shows rapid recovery at the beginning of the treatment process. For a longer period of healing, the effect of cement becomes more pronounced on healing capacity. It is suggested to treat concrete for the period of around 28 days as the hydration mechanism is almost complete in this period.
- The increase in the volume of fibers from 1% to 2.5% causes a slight improvement in the SHI of concrete. It has been shown that for low-cement concrete, the increase in SHI due to the addition of fibers is slightly more pronounced than high-cement concrete.
- The results obtained were in good agreement with the existing literature. However, more range of parameters should be included, and more relevant papers should be documented to enable meta-analysis to cover a wider range of applicability.
- The results of neural network modeling are presented as follows.
- The SHI contour plots based on crack size, total cementitious material, w/c ratio are in good agreement with the theoretical interpolation charts, whereas a moderate error has been observed in the charts of the healing period and fiber volume. This can be attributed to the lack of demarcation of data points based on more parameters.
- It is evident that optimized single-layer feed forward neural network provides a low correlation value. Hence, multilayered deep model must be developed in future study to improve the accuracy of the machine learning model.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|1||Crack Size||= initial width & = final width|
|2||Permeability Test||= initial flow & = final flow|
|3||Ultrasonic Pulse Velocity (UPV) Test|| = UPV of healed sample &|
= UPV of cracked sample
|4||Mechanical Strength|| = Strength of healed sample &|
= Strength of cracked sample
|Impact Ranking||Average MoE||Confidence Ranking|
|Period of Healing||0.207||4||0.061||1|
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Gupta, S.; Al-Obaidi, S.; Ferrara, L. Meta-Analysis and Machine Learning Models to Optimize the Efficiency of Self-Healing Capacity of Cementitious Material. Materials 2021, 14, 4437. https://doi.org/10.3390/ma14164437
Gupta S, Al-Obaidi S, Ferrara L. Meta-Analysis and Machine Learning Models to Optimize the Efficiency of Self-Healing Capacity of Cementitious Material. Materials. 2021; 14(16):4437. https://doi.org/10.3390/ma14164437Chicago/Turabian Style
Gupta, Shashank, Salam Al-Obaidi, and Liberato Ferrara. 2021. "Meta-Analysis and Machine Learning Models to Optimize the Efficiency of Self-Healing Capacity of Cementitious Material" Materials 14, no. 16: 4437. https://doi.org/10.3390/ma14164437