Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm

Al-Sodani, Khaled A. Alawi; Adewumi, Adeshina Adewale; Ariffin, Mohd Azreen Mohd; Maslehuddin, Mohammed; Ismail, Mohammad; Salami, Hamza Onoruoiza; Owolabi, Taoreed O.; Mohamed, Hatim Dafalla

doi:10.3390/ma14113049

Cited by 7 publications

(2 citation statements)

References 44 publications

(58 reference statements)

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“…Table 2 shows the proportion of the constituent materials in the alkali-activated mortar mixtures. The mixing procedure adopted in this work is the same as reported by previous researchers [19,[34][35][36][37].…”

Section: Experimental Program 221 MIX Designmentioning

confidence: 99%

Influence of Silica Modulus and Curing Temperature on the Strength of Alkali-Activated Volcanic Ash and Limestone Powder Mortar

et al. 2021

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This present study evaluates the effect of silica modulus (Ms) and curing temperature on strengths and the microstructures of binary blended alkali-activated volcanic ash and limestone powder mortar. Mortar samples were prepared using mass ratio of combined Na2SiO3(aq)/10 M NaOH(aq) of 0.5 to 1.5 at an interval of 0.25, corresponding to Ms of 0.52, 0.72, 0.89, 1.05 and 1.18, respectively, and sole 10 M NaOH(aq). Samples were then subjected to ambient room temperature, and the oven-cured temperature was maintained from 45 to 90 °C at an interval of 15 °C for 24 h. The maximum achievable 28-day strength was 27 MPa at Ms value of 0.89 cured at 75 °C. Samples synthesised with the sole 10 M NaOH(aq) activator resulted in a binder with a low 28-day compressive strength (15 MPa) compared to combined usage of Na2SiO3(aq)/10 M NaOH(aq) activators. Results further revealed that curing at low temperatures (25 °C to 45 °C) does not favour strength development, whereas higher curing temperature positively enhanced strength development. More than 70% of the 28-day compressive strength could be achieved within 12 h of curing with the usage of combined Na2SiO3(aq)/10 M NaOH(aq). XRD, FTIR and SEM + EDX characterisations revealed that activation with combined Na2SiO3(aq)/10 M NaOH(aq) leads to the formation of anorthite (CaAl2Si2O8), gehlenite (CaO.Al2O3.SiO2) and albite (NaAlSi3O8) that improve the amorphosity, homogeneity and microstructural density of the binder compared to that of samples synthesised with sole 10 M NaOH(aq).

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Section: Experimental Program 221 MIX Designmentioning

confidence: 99%

Influence of Silica Modulus and Curing Temperature on the Strength of Alkali-Activated Volcanic Ash and Limestone Powder Mortar

et al. 2021

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“…SVM finds the best classifier by searching the hyperplane with the largest margin [36]. SVR is widely adopted in many fields, including biological, behavioral research, image analysis, and medical research [37][38][39]. Along with SVR, KRR is among the most popular kernel-based methods.…”

Section: Introductionmentioning

confidence: 99%

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

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Predictive modeling of physical and mechanical properties of pervious concrete using XGBoost

Mustapha,

Abdulkareem,

Abdulkareem

et al. 2024

Neural Comput & Applic

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Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm

Cited by 7 publications

References 44 publications

Influence of Silica Modulus and Curing Temperature on the Strength of Alkali-Activated Volcanic Ash and Limestone Powder Mortar

Influence of Silica Modulus and Curing Temperature on the Strength of Alkali-Activated Volcanic Ash and Limestone Powder Mortar

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

Predictive modeling of physical and mechanical properties of pervious concrete using XGBoost

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