[HTML][HTML] Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review

C Machello, M Bazli, A Rajabipour, HM Rad… - … and Building Materials, 2023 - Elsevier
When exposed to environmental conditions, fibre-reinforced polymer (FRP) composites are
prone to material degradation. The environmental reduction factor in different structural …

[HTML][HTML] Tree-based machine learning approach to modelling tensile strength retention of Fibre Reinforced Polymer composites exposed to elevated temperatures

C Machello, KA Baghaei, M Bazli, A Hadigheh… - Composites Part B …, 2024 - Elsevier
Abstract Fibre Reinforced Polymer (FRP) composites are susceptible to degradation at
elevated temperatures. Accurate modelling of the tensile performance of FRP composites …

Performance prognosis of FRCM-to-concrete bond strength using ANFIS-based fuzzy algorithm

A Kumar, HC Arora, K Kumar, H Garg - Expert Systems with Applications, 2023 - Elsevier
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 …

Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume

S Kumar, DR Kumar, W Wipulanusat… - Journal of Building …, 2024 - Elsevier
The construction of durable and sustainable infrastructure requires the use of industrial
byproducts such as fly ash (FA) and silica fume (SF) to enhance strength and durability. This …

Using machine learning and experimental study to correlate and predict accelerated aging with natural aging of GFRP composites in hygrothermal conditions

J Wang, S Karimi, P Zeinalzad, J Zhang… - Construction and Building …, 2024 - Elsevier
Composite materials are widely used in various applications, but their mechanical and
physical properties can be significantly influenced by environmental factors such as …

[HTML][HTML] A neural network based digital twin model for the structural health monitoring of reinforced concrete bridges

T Hielscher, S Khalil, N Virgona, SA Hadigheh - Structures, 2023 - Elsevier
Abstract Developments in Structural Health Monitoring (SHM) research over the past few
decades have demonstrated potential in optimising maintenance solutions for degrading …

Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence …

R Kazemi, A Gholampour - Construction and Building Materials, 2023 - Elsevier
One of the major known challenges of improving durability of concrete structures is reducing
the permeability of concrete to retard the transport of chloride ions. In this regard, the …

[HTML][HTML] Enhancing carbon fibre recovery through optimised thermal recycling: Kinetic analysis and operational parameter investigation

Y Wei, SA Hadigheh - Materials Today Sustainability, 2024 - Elsevier
Thermal recycling is an attractive method for recovering carbon fibre from carbon fibre-
reinforced polymer (CFRP) composite waste. The main drawbacks of the thermal recycling …

[HTML][HTML] Prediction of FRCM–concrete bond strength with machine learning approach

A Kumar, HC Arora, K Kumar, MA Mohammed… - Sustainability, 2022 - mdpi.com
Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and
restoration of building structures. The bond strength between FRCM and concrete typically …

[HTML][HTML] Artificial neural network prediction of transverse modulus in humid conditions for randomly distributed unidirectional fibre reinforced composites: a …

KA Baghaei, SA Hadigheh - Composite Structures, 2024 - Elsevier
This paper proposes an innovative micromechanics-based artificial neural network (ANN)
method to efficiently investigate the transverse modulus of unidirectional fibre/epoxy …