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 …
[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 …
[HTML][HTML] Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
This paper presents a data-driven approach to determine the load and flexural capacities of
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …
Soft computing-based models for the prediction of masonry compressive strength
Masonry is a building material that has been used in the last 10.000 years and remains
competitive today for the building industry. The compressive strength of masonry is used in …
competitive today for the building industry. The compressive strength of masonry is used in …
Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns
AS Bakouregui, HM Mohamed, A Yahia… - Engineering …, 2021 - Elsevier
This study presents a new approach for predicting the load-carrying capacity of reinforced
concrete (RC) columns reinforced with fiber-reinforced polymer (FRP) bars with an eXtreme …
concrete (RC) columns reinforced with fiber-reinforced polymer (FRP) bars with an eXtreme …
Prediction of ultimate condition of FRP-confined recycled aggregate concrete using a hybrid boosting model enriched with tabular generative adversarial networks
Despite its multiple benefits, recycled aggregate concrete (RAC) usually exhibits inferior
properties compared with natural aggregate concrete, which has been deemed as a hurdle …
properties compared with natural aggregate concrete, which has been deemed as a hurdle …
Machine learning-based prediction of CFST columns using gradient tree boosting algorithm
Among recent artificial intelligence techniques, machine learning (ML) has gained
significant attention during the past decade as an emerging topic in civil and structural …
significant attention during the past decade as an emerging topic in civil and structural …
[HTML][HTML] Tree-based machine learning approach to modelling tensile strength retention of Fibre Reinforced Polymer composites exposed to elevated temperatures
Abstract Fibre Reinforced Polymer (FRP) composites are susceptible to degradation at
elevated temperatures. Accurate modelling of the tensile performance of FRP composites …
elevated temperatures. Accurate modelling of the tensile performance of FRP composites …
Predicting shear strength of FRP-reinforced concrete beams using novel synthetic data driven deep learning
Abstract Machine learning algorithms have emerged as a powerful technique to predict the
engineering properties of composite materials and structures where traditional statistical …
engineering properties of composite materials and structures where traditional statistical …
Behavior of FRP Ring-Confined CFST columns under axial compression
JJ Zeng, YW Zheng, F Liu, YC Guo, C Hou - Composite Structures, 2021 - Elsevier
Although concrete-filled steel tube (CFST) columns have been widely accepted in
applications, they suffer deterioration due to various reasons. On the other hand, the …
applications, they suffer deterioration due to various reasons. On the other hand, the …