This paper investigates the effect of fiber-reinforced composites (FRPs) on the mechanical properties of concrete under ambient conditions. It begins with an examination of the various types of FRP and their advantages, followed by a review of isostructural models for passively restrained concrete under ambient conditions. These models are categorized into two main groups: those assuming constant confining stresses and those that incorporate stress constraints related to the loading history. Recent studies have highlighted the significant role of stress paths in determining the stress-strain behavior of concrete. Traditional methods for predicting the FRP-constrained concrete reinforcement bond at room temperature are increasingly being replaced by machine learning techniques, such as Artificial Neural Networks (ANNs) and Genetic Expression Programming (GEP), which offer superior accuracy in predicting the FRP-constrained concrete bond strength and the compressive properties of FRP-confined concrete columns. In particular, experimental results show that the compressive strength of FRP-confined concrete columns can increase by up to 30-250%. This review offers valuable insights into the effects of FRP on concrete and contributes to the advancement of engineering design practices.
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http://dx.doi.org/10.3390/ma18051151 | DOI Listing |
Materials (Basel)
March 2025
The Southern Scientific Center of the Russia Academy of Sciences, Moscow 4119049, Russia.
This paper investigates the effect of fiber-reinforced composites (FRPs) on the mechanical properties of concrete under ambient conditions. It begins with an examination of the various types of FRP and their advantages, followed by a review of isostructural models for passively restrained concrete under ambient conditions. These models are categorized into two main groups: those assuming constant confining stresses and those that incorporate stress constraints related to the loading history.
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March 2025
Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, 150090, People's Republic of China.
Aluminum alloy has been widely used in modern engineering structures due to its good corrosion resistance, light-weight, convenient processing, and recyclability. To study the bonding behavior of concrete-filled aluminum alloy tubes (CFAT) columns and obtain the bond strength formula and bond-slip constitutive model of CFAT, the push-out tests of three circular and three square CFAT specimens were conducted. The failure patterns, load-slip/strain curves and the stress distribution of the stubs were investigated.
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February 2025
School of Urban Construction, Changzhou University, Changzhou 213164, China.
A novel L-shaped concrete-filled steel tube (CFST) column is proposed in this study. A finite element model of the column is developed using ABAQUS software to analyze its load transfer mechanism and axial compressive behavior. The effects of factors such as the steel strength, steel tube thickness, support plate configuration, and perforation of the support plates on the compressive performance of the column are investigated.
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February 2025
Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta, 34517, Egypt.
Confined columns, such as round-ended concrete-filled steel tubular (CFST) columns, are integral to modern infrastructure due to their high load-bearing capacity and structural efficiency. The primary objective of this study is to develop accurate, data-driven approaches for predicting the axial load-carrying capacity (P) of these columns and to benchmark their performance against existing analytical solutions. Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models - LightGBM, XGBoost, and CatBoost - and three deep learning (DL) models - Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM).
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January 2025
Building Materials Engineering Laboratory, Department of Architecture, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan.
This study presents a novel approach to the design and assessment of slender reinforced concrete (RC) columns by integrating Brillouin Optical Time Domain Analysis (BOTDA) for real-time, distributed strain monitoring and introducing a "time-dependent deterioration factor" strain decay (η). Experimental tests on 200 mm × 200 mm RC columns with lengths of 1800 mm and slenderness ratios of 29.4, reinforced with four 12 mm bars, captured strain variations up to 400 microstrain under an axial load of 1200 kN, demonstrate BOTDA's sensitivity and precision.
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