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On-Going Works

Analysing the Influence of Oxide Ratio of Alumina Silicate Precursor on the Development of Geopolymer Materials and further validation using machine learning​

Contributors : Ashwin Rawat, Sanjog Chhetri Sapkota, Prasenjit Saha, and Sourav Das

Highlights :

  • The main goal of this paper is to look at how the amounts of major oxides (Si/Al, Al/Na, Si/Na, and Na/H2O) affect the compressive strength of geopolymer blocks.
  • In a laboratory environment, numerous factors can be adjusted and regulated, such as the choice and concentration of the solution, curing conditions, and mixing parameters.It is crucial to remember that the researcher cannot control the percentage of oxides present in the source material.Therefore, this study plays a crucial role in addressing this uncontrollable variable in the realm of geopolymer concrete development.
  • This study further validates by using the machine learning algorithm for the prediction of the characteristics.

Status : Planning to send in Construction and building materials (I.F. 7.1)

Prediction of particulate matters of Kathmandu valley using attention-based Deep learning Techniques​

Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN) architectures are used to train time series deep learning models. Additionally, I actively participated in the composition of a manuscript for research documentation.

Empirical approach for Prediction of Laboratory Peak Shear Stress Along the Cohesive Soil-Geosynthetic Interface using optimized machine learning model​

  • A laboratory peak shear stress data is used in the data .
  • Trained using an optimal boosting ML model using Python.
  • Explainable behavior of the optimized model for decision making.
  • Comparative study with standalone and ensemble machine learning model. -final