Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer

Cao, Yan; Fan, Qingyang; Azar, Sadaf Mahmoudi; Alyousef, Rayed; Yousif, Salim T.; Wakil, Karzan; Jermsittiparsert, Kittisak; Ho, Lanh Si; Alabduljabbar, Hisham; Alaskar, Abdulaziz

doi:10.1016/j.istruc.2020.05.031

Cited by 22 publications

(7 citation statements)

References 49 publications

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“…SMAs for RC beams have been tested in just a few studies since reinforced SMA concrete beams can be damaged in earthquake events and are costly. Few studies have studied the properties of these materials with NNs [ 93 , 94 ]. The moment of inertia is a studied parameter of SMA RC that is a function of the reduction factor of the strength; this parameter reflects the deflection of the SMA.…”

Section: Sma Forms and Ann Applicationsmentioning

confidence: 99%

Review of Neural Network Modeling of Shape Memory Alloys

Hmede

Chapelle

Lapusta

2022

Sensors

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Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning.

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Section: Sma Forms and Ann Applicationsmentioning

confidence: 99%

Review of Neural Network Modeling of Shape Memory Alloys

Hmede

Chapelle

Lapusta

2022

Sensors

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“…the estimation of capacity, strength, displacement, deformation, some coefficients in structural computations, behavior, etc. based on element, material, and structure [7][8][9][10][11][12][13][14][15]. The second heading includes the studies in which assessments such as fire, seismic, condition, damage, performance, and capacity were made [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

1D Fuzzy inverse logic method and its use in the design of thick reinforced concrete columns

ÖZTEKİN

2024

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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In this study, without using any uniaxial force and bending moment (N-M) interaction diagrams, designs were carried out on thick columns subjected to uniaxial bending and compression by a novel 1 dimensional (1D) Fuzzy Inverse Logic (FIL) method. For this purpose, firstly, a Fuzzy Logic (FL) model was developed and the FIL method was applied to it thereafter. While, the cross-section width (b), the cross-section height (h), the rebar diameter(), the numbers of reinforcement rows (Rx and Ry) placed into the cross-section in X and Y directions, the characteristic concrete compressive strength(fck) and the axial force ratio Nr=N/(b.h.(fck/1.5)) were taken as variable parameters, concrete cover thickness (c), rebar strength (fyd) and k1 parameter defined for the concrete pressure block were kept constant in the developed FL model. After designs were performed on 15 columns having different variable variations by the 1D FIL method, moment bearing capacities of the obtained 9737 alternative designs determined conventionally were compared with the desired moment values. The evaluations made on the comparisons show that the FIL method is not only a very effective artificial intelligence method for the design of reinforced concrete thick columns but also a promising method for many other problems such as control, optimization, design, etc.

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“…A reasonable in-service safety margin for the structural design of a given concrete structure requires that its concrete components remain within the elastic zone [4]. Thus, the modulus of elasticity is a fundamental parameter for the structural design of concrete components, the accurate estimation of which is highly relevant in this process [5].…”

Section: Introductionmentioning

confidence: 99%

Elastic stiffness estimation of aggregate–ITZ system of concrete through matrix porosity and volumetric considerations: explanation and exemplification

Revilla‐Cuesta

Skaf

Santamarı́a

et al. 2022

Archiv.Civ.Mech.Eng

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The modulus of elasticity of a concrete depends on the elastic stiffness of both the cementitious matrix and the aggregate–ITZ system, which includes any slippage mechanisms under loading between the aggregate and the cementitious matrix within the interfacial transition zone (ITZ). A procedure is presented in this paper to estimate the elastic stiffness of an aggregate–ITZ system within a cementitious matrix, by considering the relative volumes and the porosities of the concrete components. The method was validated by determining the elastic stiffness of both the limestone–ITZ and the electric arc furnace slag (EAFS)–ITZ systems when embedded in a slag-based cementitious matrix. The greater stiffness of the EAFS–ITZ system in comparison with the natural aggregate system explained the higher strength and modulus of elasticity of the concrete following additions of EAFS. Moreover, having determined those parameters, the elastic moduli of concretes with a similar cementitious matrix could then be accurately estimated.

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Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer

Cited by 22 publications

References 49 publications

Review of Neural Network Modeling of Shape Memory Alloys

Review of Neural Network Modeling of Shape Memory Alloys

1D Fuzzy inverse logic method and its use in the design of thick reinforced concrete columns

Elastic stiffness estimation of aggregate–ITZ system of concrete through matrix porosity and volumetric considerations: explanation and exemplification

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