[HTML][HTML] Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review
When exposed to environmental conditions, fibre-reinforced polymer (FRP) composites are
prone to material degradation. The environmental reduction factor in different structural …
prone to material degradation. The environmental reduction factor in different structural …
Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC)
This study aims to provide an effective and accurate machine learning approach to predict
the compressive strength (CS) and flexural strength (FS) of 3D printed fiber reinforced …
the compressive strength (CS) and flexural strength (FS) of 3D printed fiber reinforced …
Performance prognosis of FRCM-to-concrete bond strength using ANFIS-based fuzzy algorithm
Nowadays, strengthening of reinforced concrete structures with a new class of sustainable
materials is the possible solution to retrofit the aged deteriorated structures. It is difficult to …
materials is the possible solution to retrofit the aged deteriorated structures. It is difficult to …
Ensemble machine learning-based models for estimating the transfer length of strands in PSC beams
This study aims to develop four ensemble machine learning (ML) models, including Random
Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme …
Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme …
Machine-learning-based models versus design-oriented models for predicting the axial compressive load of FRP-confined rectangular RC columns
YAK Sayed, AA Ibrahim, AG Tamrazyan… - Engineering Structures, 2023 - Elsevier
To improve the prediction accuracy of axial compressive load of FRP-confined concrete
columns, machine-learning techniques have been used recently. However, few studies have …
columns, machine-learning techniques have been used recently. However, few studies have …
EAD-DNN: Early Alzheimer's disease prediction using deep neural networks
P Thangavel, Y Natarajan, KRS Preethaa - Biomedical Signal Processing …, 2023 - Elsevier
Early Alzheimer's disease (EAD) diagnosis enables individuals to take preventative actions
before irreversible brain damage occurs. Memory and thinking skills get worse in alzheimer …
before irreversible brain damage occurs. Memory and thinking skills get worse in alzheimer …
High‐Performance Concrete Strength Prediction Based on Machine Learning
Y Liu - Computational Intelligence and Neuroscience, 2022 - Wiley Online Library
High‐performance concrete is a new high‐tech concrete, produced using conventional
materials and processes, with all the mechanical properties required for concrete structures …
materials and processes, with all the mechanical properties required for concrete structures …
Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis
Interfacial tension (IFT) is a key physical property that affects various processes in the oil and
gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability …
gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability …
Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings
KR Sri Preethaa, SD Munisamy, A Rajendran… - Sensors, 2023 - mdpi.com
Earthquakes are cataclysmic events that can harm structures and human existence. The
estimation of seismic damage to buildings remains a challenging task due to several …
estimation of seismic damage to buildings remains a challenging task due to several …
An interpretable machine learning model for predicting bond strength of CFRP-steel epoxy-bonded interface
This study develops an interpretable machine learning model for predicting the bond
strength of CFRP-steel epoxy bonding interfaces and reveals key bond parameters. A total …
strength of CFRP-steel epoxy bonding interfaces and reveals key bond parameters. A total …