Experimental and Machine Learning Approaches for Predicting the Unconfined Compressive Strength of Stabilized Black Cotton Soil

Machine Learning and Explainable Artificial Intelligence for Predicting Unconfined Compressive Strength in Carbonated Stabilized Soils

Innovative sustainable concrete with waste glass materials: an explainable machine learning for compressive strength prediction

Sustainable concrete incorporating waste glass materials has emerged as a promising solution to reduce environmental impacts associated with cement production and natural aggregate depletion. Accurate prediction of compressive strength (CS) is essential for optimizing such mixtures and ensuring structural reliability....

Experimental Investigation and Machine Learning-Based Prediction of Marshall Stability of Stone Mastic Asphalt Using Steel Slag

This study applies several machine learning models to predict the Marshall stability of Stone Mastic Asphalt mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates, with CatBoost providing the best performance.

Comparative and Interpretable Machine Learning for Fly Ash Geopolymer Strength: Prediction, Validation and Optimization

An interpretable machine learning framework integrating Extreme Gradient Boosting with Shapley Additive Explanations to predict the 28-day compressive strength of fly ash-based geopolymer concrete (FA-GPC) is developed and experimentally validates.

Development of machine learning models to estimate strength characteristics of combined cellulose xanthate and fibre reinforced soft clayey soil used as subgrade

Optimization and predictive measurement of compressive strength of iron ore slag modified concrete using data-driven supervised machine learning algorithms.

An integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag is developed and the hybrid XGB-GBR model demonstrates the highest balanced performance.