Machine learning for structural engineering: A state-of-the-art review
HT Thai - Structures, 2022 - Elsevier
Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …
intelligence (AI). It provides a unique opportunity to make structural engineering more …
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] Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms
To explore the complicated functional relationship between key parameters such as the
recycled aggregate properties, mix proportion and compressive strength of recycled …
recycled aggregate properties, mix proportion and compressive strength of recycled …
Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model
Q Li, Z Song - Journal of Cleaner Production, 2023 - Elsevier
By replacing cement in concrete production with rice husk ash (RHA), the amount of cement
used and its environmental impact can be reduced. The objective of this study is to …
used and its environmental impact can be reduced. The objective of this study is to …
Shield attitude prediction based on Bayesian-LGBM machine learning
Effective shield attitude control is essential for the quality and safety of shield construction.
The traditional shield attitude control method is manual control based on a driver's …
The traditional shield attitude control method is manual control based on a driver's …
[HTML][HTML] NN-EUCLID: Deep-learning hyperelasticity without stress data
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …
Recent advances and applications of machine learning in experimental solid mechanics: A review
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators
SK Parhi, SK Patro - Journal of Building Engineering, 2023 - Elsevier
Geopolymer concrete (GPC) has the potential to replace conventional concrete. But, the
mixed proportion of GPC poses several difficulties due to various contributing factors. The …
mixed proportion of GPC poses several difficulties due to various contributing factors. The …
Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning
In recent years, alkali activated material (AAM) or geopolymer has emerged as a sustainable
and eco-friendly alternative to cement. It is owing to its low power consumption and …
and eco-friendly alternative to cement. It is owing to its low power consumption and …
Development of machine learning models for the prediction of the compressive strength of calcium-based geopolymers
W Huo, Z Zhu, H Sun, B Ma, L Yang - Journal of Cleaner Production, 2022 - Elsevier
Compressive strength is an important mechanical index that determines the mixture design
of geopolymer, and its accurate prediction is essential. The existing experiment-based and …
of geopolymer, and its accurate prediction is essential. The existing experiment-based and …