Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP
This study presents the development of new empirical prediction models to evaluate swell
pressure and unconfined compression strength of expansive soils (P s UCS-ES) using three …
pressure and unconfined compression strength of expansive soils (P s UCS-ES) using three …
Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms
In this study, gene expression programming (GEP) and multi gene expression programming
(MEP) are utilized to formulate new prediction models for determining the compaction …
(MEP) are utilized to formulate new prediction models for determining the compaction …
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 …
Symbolic regression in materials science
The authors showcase the potential of symbolic regression as an analytic method for use in
materials research. First, the authors briefly describe the current state-of-the-art method …
materials research. First, the authors briefly describe the current state-of-the-art method …
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 …
[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) …
[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] Development of predictive models for sustainable concrete via genetic programming-based algorithms
Waste foundry sand (WFS), a by-product of the casting industry, is a potential material that
may be employed as a substitute for fine aggregate in concrete. In the present study, gene …
may be employed as a substitute for fine aggregate in concrete. In the present study, gene …
A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector
Alkali-activated materials (AAMs) have recently gained attention as potentially useful
alternative binders that can reduce carbon dioxide emissions initiated by the production of …
alternative binders that can reduce carbon dioxide emissions initiated by the production of …
Predicting the ultimate axial capacity of uniaxially loaded cfst columns using multiphysics artificial intelligence
The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop
a prediction Multiphysics model for the circular CFST column by using the Artificial Neural …
a prediction Multiphysics model for the circular CFST column by using the Artificial Neural …