Transfer-learned stacked models for assessing mechanical and environmental properties of glass powder UHPC

Generative data augmentation, explainable machine learning, and uncertainty analysis for compressive strength prediction of sustainable UHPC

Recent interest in ultra-high-performance concrete (UHPC) has been on the use of construction and demolition waste (CDW) due to the growing need to use sustainable construction materials. Recycled concrete powder (RCP), a product of CDW, is a promising way to reduce cement use, lower CO2 emissions, and make green const...

An ensemble approach to predict the compressive strength of ultra-high-performance concrete

Ultra-High-Performance Concrete (UHPC) is a state-of-the-art concrete technology with exceptional qualities, including high compressive strength (CS) and durability. The CS, an essential property of UHPC, is determined through costly, time-consuming studies that require large amounts of material. To overcome these cons...

Novel interpretable machine learning models for predicting compressive strength of nano-silica concrete

This study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design and highlights the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and suppo...

Machine Learning for Performance Prediction of Ultra-High Performance Concrete

This paper provides a comprehensive review of ML applications in UHPC, focusing on the prediction of compressive strength, flexural strength, workability, and durability properties.

Hyperparameter-Optimized machine learning approach for compressive strength prediction of metakaolin concrete reinforced with glass fiber

Proxy Feature-Based Interpretable Machine Learning to Predict Multi-Properties of Ultra-High-Performance Concrete

Raw mix-design variables used to predict ultra-high-performance concrete (UHPC) properties cannot fully represent internal proportions and structural compatibility. This study develops mechanism-informed proxy-core features based on particle packing, water film thickness, and rheology, and evaluates four feature system...