Effective carbon footprint assessment strategy in fly ash geopolymer concrete based on adaptive boosting learning techniques.

In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO-FP) of fly ash geopolymer concrete. The approach employs adaptive boosting to enhance decision tree regression (DTR) and support vector regression (SVR), resulting in a robust predictive framework. The models used key material features, including fly ash concentration, fine and coarse aggregates, superplasticizer, curing temperature, and alkali activator levels.

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques.

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance.

Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis.

Earth materials have been used in construction as safe, healthy and environmentally sustainable. It is often challenging to develop an optimum soil mix because of the significant variations in soil properties from one soil to another. The current study analyzed the soil properties, including the grain size distribution, Atterberg limits, compaction characteristics, etc.

Confined phase separation of aqueous-organic nanodroplets.

Nano-confined supercooled water occurs frequently in aqueous-organic aerosol nanodroplets that are ubiquitous in the atmosphere and in many industrial processes such as natural gas refining. The structure of these nanodroplets is important because it influences droplet growth and evaporation rates, nucleation rates, and radiative properties. We used classical molecular dynamics (MD) simulations to study the structures of binary water-butanol nanodroplets for several temperatures and droplet sizes.