Publications

This study explores applications of AI, machine learning, and deep learning in green transportation. It examines algorithms including Genetic Algorithm, Support Vector Machine, Naive Bayes, k-means clustering, CNN, and RNN, highlighting their role in enabling greener, safer, and more efficient mobility solutions while combating climate change.

This research applies ensemble machine learning algorithms to predict the compressive strength of concrete incorporating tyre rubber and brick powder as partial replacements. The study evaluates multiple ML models to identify the most accurate predictive framework for sustainable concrete design.

This study applies machine learning algorithms to evaluate and predict the compressive strength of laterite blocks made with metakaolin-based geopolymer and sugarcane molasses, contributing to sustainable construction material development in resource-limited regions.

This paper uses machine learning techniques to predict compressive strength of laterite blocks stabilized with metakaolin geopolymer and sugarcane molasses, providing a data-driven approach to optimizing sustainable building materials.

This review analyzes secondary data on nanoparticle integration in board production, evaluating relationships among physical properties (water absorption, thickness swelling) and mechanical properties (modulus of rupture, modulus of elasticity, internal bond strength) using machine learning algorithms.

This research employs machine learning algorithms to predict the flexural and split tensile strength of waste glass-concrete composites, advancing sustainable construction through data-driven material optimization.

This paper reviews the integration of artificial intelligence in urban environments, examining current applications, challenges, and ethical considerations surrounding Urban-AI deployment in smart city development.

This study evaluates the performance of machine learning algorithms including Pearson correlation, hierarchical clustering, and decision tree models to predict physical and mechanical properties of nanoparticle-enhanced panels, with results showing improved mechanical properties from nanoparticle integration.

This study reviews physical and mechanical properties of wood panels combined with three types of plastic (PET, PP, HDPE) and applies machine learning algorithms to predict material performance, contributing to eco-friendly construction material development.

This study analyzes nanoparticle effects on concrete mechanical properties using advanced machine learning algorithms, examining various nanoparticle types including MWCNTs, graphene nanoplatelets, nano-SiO2, and nano-TiO2, and their impact on flexural, compressive, and tensile strengths.