Sustainable use of chemically modified tyre rubber in concrete: Machine learning based novel predictive model
To encourage the consumption of crumb rubber (CR), gene expression programming (GEP)
has been exercised in this paper to establish empirical models for estimation of mechanical …
has been exercised in this paper to establish empirical models for estimation of mechanical …
A scientometrics review of soil properties prediction using soft computing approaches
In this world, several types of soils are available with their different engineering properties.
Determining each soil's engineering properties is difficult because the laboratory procedures …
Determining each soil's engineering properties is difficult because the laboratory procedures …
Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis
This study investigates the non-linear capabilities of two machine learning prediction
models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride …
models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride …
[HTML][HTML] Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models
A comparison between deep learning and standalone models in predicting the compaction
parameters of soil is presented in this research. One hundred and ninety and fifty-three soil …
parameters of soil is presented in this research. One hundred and ninety and fifty-three soil …
[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) …
New prediction models for the compressive strength and dry-thermal conductivity of bio-composites using novel machine learning algorithms
Bio-composites have become the prime material selection for green concrete because of the
increasing awareness of environmental issues. Due to their highly heterogenous nature …
increasing awareness of environmental issues. Due to their highly heterogenous nature …
Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-
friendly approach to waste reduction and enhancing cementitious materials. However …
friendly approach to waste reduction and enhancing cementitious materials. However …
[HTML][HTML] Smart prediction of liquefaction-induced lateral spreading
The prediction of liquefaction-induced lateral spreading/displacement (D h) is a challenging
task for civil/geotechnical engineers. In this study, a new approach is proposed to predict D h …
task for civil/geotechnical engineers. In this study, a new approach is proposed to predict D h …
[HTML][HTML] Optimizing durability assessment: Machine learning models for depth of wear of environmentally-friendly concrete
The use of fly ash in cementitious composites has gained popularity. However, assessing
the depth of wear (DW) of concrete requires expensive and destructive laboratory tests …
the depth of wear (DW) of concrete requires expensive and destructive laboratory tests …
[HTML][HTML] Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures
Steel-fiber-reinforced concrete (SFRC) has emerged as a viable and efficient substitute for
traditional concrete in the construction industry. By incorporating steel fibers into the …
traditional concrete in the construction industry. By incorporating steel fibers into the …