Prediction of concrete and FRC properties at high temperature using machine and deep learning: a review of recent advances and future perspectives
Concrete structures when exposed to elevated temperature significantly decline their
original properties. High temperatures substantially affect the concrete physical and …
original properties. High temperatures substantially affect the concrete physical and …
[HTML][HTML] Performance characteristics of cementitious composites modified with silica fume: A systematic review
The intention of this study was to examine the use of the most prevalent industrial byproduct,
silica fume, as a supplementary cementitious material (SCM). Along with the typical review …
silica fume, as a supplementary cementitious material (SCM). Along with the typical review …
[HTML][HTML] A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations …
Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …
Comparative study of advanced computational techniques for estimating the compressive strength of UHPC
The effect of raw materials on the compressive strength of concrete is a complex process,
especially in the case of ultra-high-performance concrete (UHPC), where a higher number of …
especially in the case of ultra-high-performance concrete (UHPC), where a higher number of …
Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
Y Wu, Y Zhou - Construction and Building Materials, 2022 - Elsevier
The application of the traditional support vector regression (SVR) model to predict the
compressive strength of concrete faces the challenge of parameter tuning. To this end, a …
compressive strength of concrete faces the challenge of parameter tuning. To this end, a …
Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations …
One-part alkali-activated material (AAM) is a new eco-friendly developed low-carbon binder
that utilizes alkaline activators in solid form. This study deals with the experimental synthesis …
that utilizes alkaline activators in solid form. This study deals with the experimental synthesis …
Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis
Fiber-reinforced self-compacting concrete (FRSCC), a great combination of self-compacting
concrete (SCC) and fiber, plays a vital role as a potential construction material. Improving …
concrete (SCC) and fiber, plays a vital role as a potential construction material. Improving …
[HTML][HTML] Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer
This study used three artificial intelligence-based algorithms–adaptive neuro-fuzzy inference
system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) …
system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) …
Machine learning prediction models to evaluate the strength of recycled aggregate concrete
Compressive and flexural strength are the crucial properties of a material. The strength of
recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate …
recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate …
[HTML][HTML] Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC
(Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With …
(Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With …