An improved deflection model for FRP RC beams using an artificial intelligence-based approach

Nguyen, Hannah; Zhang, Qianhui; Choi, Eunsoo; Duan, Wenhui

doi:10.1016/j.engstruct.2020.110793

Cited by 14 publications

(10 citation statements)

References 70 publications

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“…In this study, the service load is assumed to be 40% of the ultimate load. Figure 6 a shows that for the GFRP specimen corresponding to the balanced reinforcement ratio, the models of effective moment of inertia proposed by ACI 440.1R-03 [ 32 ], ACI 440.1R-06 [ 34 ], and Nguyent et al [ 39 ] were found to underestimate the values of all states of the load after cracking. The model of Toutanji and Saafi [ 29 ] was shown to underestimate deflection at the service load stage by predicting too much stiffness after the initial load but overestimating the ultimate load.…”

Section: Comparison Of Test Resultsmentioning

confidence: 99%

“…For BFRP specimens, the equations of Bishoff [ 36 , 37 ], Bischoff and Gross [ 38 ], Mousavi and Esfahani [ 22 ], and our proposed model predicted a rather accurate error, with the average deflection within 1 mm. In the ultimate load state, models from ACI 440.1R-06 [ 34 ], Rafi and Nadjai [ 35 ], and Nguyen et al [ 39 ] accurately evaluated the average deflection error within 1 mm. It is judged that the fluctuation is large because the deflection generated under the same load is different for each GFRP and BFRP specimen.…”

Section: Comparison Of Test Resultsmentioning

confidence: 99%

“…Neuyen et al [ 39 ] proposed an equation of the effective moment of inertia using an AI technique called gene expression programming (GEP). They concluded that the proposed models provide good predictions of deflections of FRP-reinforced beams in comparison with experimental data and results from several existing design codes:

…”

Section: Effective Moment Of Inertia and Predictive Proposal Modelmentioning

confidence: 99%

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Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an Optimization Algorithm

Jang

Kim

2023

Materials

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FRP (fiber-reinforced polymer)-reinforced concrete members have larger deflection than reinforced concrete members because of the low modulus of elasticity of the FRP bar. In this paper, we proposed a new effective moment of inertia equation to predict the deflection of FRP-reinforced concrete members based on the harmony search algorithm. The harmony search algorithm is used to optimize a function that minimizes the error between the deflection value of the experimental result and the deflection value expected from the specimen’s specifications. In the experimental part, four GFRP (Glass Fiber-Reinforced Polymer)- and BFRP (Basalt Fiber-Reinforced Polymer)-reinforced concrete slab specimens were manufactured and tested. FRP-reinforced concrete slabs were reinforced with GFRP and BFRP rebars on spiral rib surfaces. The effects of the FRP reinforcement ratio and balanced reinforcement ratio (ρf/ρfb), the moment of inertia of the transformed cracked section and the gross moment of inertia (Icr/Ig), and the cracking moment and the maximum service load moment (Mcr/Ma) on the effective moment of inertia have been considered. The experimental results and predicted results of the flexural testing of concrete slabs reinforced with FRP rebars were compared, and the experimental results were in good agreement with the calculated values using the proposed effective moment of inertia equation.

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Section: Comparison Of Test Resultsmentioning

confidence: 99%

Section: Comparison Of Test Resultsmentioning

confidence: 99%

…”

Section: Effective Moment Of Inertia and Predictive Proposal Modelmentioning

confidence: 99%

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Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an Optimization Algorithm

Jang

Kim

2023

Materials

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“…Improvements to the classical equations to calculate deflections are permanently being studied to consider, among other factors, the behaviour of non-rectangular cross-sections (Shaaban and Mustafa, 2019) and the behaviour of new reinforcement solutions (Ge et al, 2020), or to perfect the calculation of the effective moment of inertia (Nguyen et al, 2020).…”

Section: 4mentioning

confidence: 99%

A path to more versatile code provisions for slab deflection control

Sanabra-Loewe

Capellà-Llovera

Ramírez-Anaya³

et al. 2023

Proceedings of the Institution of Civil Engineers - Structures

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An assessment of the methods of codes ACI 318 and Eurocode 2 is performed to establish a minimum slenderness for reinforced concrete slabs, which is a simplified method for controlling the deflection of slabs. This assessment is performed by comparing these methods with each other and with two further methods that have inspired, among others, the Australian standard for concrete structures. The variables evaluated include those that most influence deflection control (span, load and allowable deflection). The study cases include spans that range from 6 m (20 ft) to 12 m (40 ft) and loads that range from 5 kN/m2 (104 lb/ft2) to 15 kN/m2 (313 lb/ft2). The results of the study reveal some of the key advantages and shortcomings of the ACI 318 and Eurocode 2 provisions. Suggestions are provided to improve the current provisions of the codes. Methods that are not too complicated but more refined can help the code provisions to become more versatile. The aim of this work is to minimise potential problems caused by designing excessively thick slabs (heavy and pollutant) or excessively slender slabs (too deformable).

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“…A gene expression programming (GEP) approach was used by Nguyena et al [67] to predict the deflection of FRP-strengthened beams. The work trained GEP using a database developed by calculating the effective moment of inertia of 108 constructed beams using 10 equations taken from the literature, taking into consideration the benefits of both theoretical and empirical models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

et al. 2022

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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 takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time.

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An improved deflection model for FRP RC beams using an artificial intelligence-based approach

Cited by 14 publications

References 70 publications

Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an Optimization Algorithm

Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an Optimization Algorithm

A path to more versatile code provisions for slab deflection control

Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

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