[HTML][HTML] Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques
E Champa-Bujaico, AM Díez-Pascual… - Composites Part B …, 2024 - Elsevier
Abstract Machine learning (ML) models provide fast and accurate predictions of material
properties at a low computational cost. Herein, the mechanical properties of multiscale poly …
properties at a low computational cost. Herein, the mechanical properties of multiscale poly …
[HTML][HTML] Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Abstract Three-dimensional (3D) printing in the construction industry is growing rapidly due
to its inherent advantages, including intricate geometries, reduced waste, accelerated …
to its inherent advantages, including intricate geometries, reduced waste, accelerated …
[HTML][HTML] Tribological properties of CNT-filled epoxy-carbon fabric composites: optimization and modelling by machine learning
Polymer matrix composites reinforced with fibers/fillers are extensively used in several
tribological components of automotive and boating applications. The mechanical …
tribological components of automotive and boating applications. The mechanical …
[HTML][HTML] Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and …
D Yang, P Xu, A Zaman, T Alomayri, M Houda… - Journal of Materials …, 2023 - Elsevier
The strength of carbon nanotubes (CNTs) and cement composites is dependent on multiple
variables. In addition, CNTs added to a cement-based matrix can boost its strength …
variables. In addition, CNTs added to a cement-based matrix can boost its strength …
Comparison of traditional and automated machine learning approaches in predicting the compressive strength of graphene oxide/cement composites
The prediction of the compressive strength (CS) of graphene oxide reinforced cement
composites (GORCCs) is crucial for accelerating their potential application in civil …
composites (GORCCs) is crucial for accelerating their potential application in civil …
[HTML][HTML] Prediction of phytoplankton biomass and identification of key influencing factors using interpretable machine learning models
Y Xu, D Zhang, J Lin, Q Peng, X Lei, T Jin, J Wang… - Ecological …, 2024 - Elsevier
The water quality of the Middle Route of the South-to-North Water Diversion Project (MRP) of
China is related to the health and safety of about 8500w people. In recent years, multiple …
China is related to the health and safety of about 8500w people. In recent years, multiple …
[HTML][HTML] The use of machine learning techniques to investigate the properties of metakaolin-based geopolymer concrete
The construction industry significantly contributes to global greenhouse gas emissions,
highlighting the imperative for developing environmentally friendly construction materials …
highlighting the imperative for developing environmentally friendly construction materials …
Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine …
E Champa-Bujaico, AM Díez-Pascual, P García-Díaz - Biomolecules, 2023 - mdpi.com
Predicting the mechanical properties of multiscale nanocomposites requires simulations that
are costly from a practical viewpoint and time consuming. The use of algorithms for property …
are costly from a practical viewpoint and time consuming. The use of algorithms for property …
Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely …
Calcium sulfoaluminate (CSA) cement mixture design is challenging due to the influence of
multiple features on its unconfined compressive strength (UCS). Consequently, the …
multiple features on its unconfined compressive strength (UCS). Consequently, the …
Predicting the mechanical properties of pristine and defective carbon nanotubes using a random forest model
Data-driven models have lately emerged as a faster and less time-consuming method for
computing material properties than computationally expensive conventional molecular …
computing material properties than computationally expensive conventional molecular …