Strengthening and prestressing of bridge decks with ribbed iron-based shape memory alloy bars

Schranz, Bernhard; Michels, Julien; Czaderski, Christoph; Motavalli, Masoud; Vogel, Thomas; Shahverdi, Moslem

doi:10.1016/j.engstruct.2021.112467

Cited by 63 publications

(25 citation statements)

References 34 publications

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“…The flexural strengthening of RC members with prestressed FeSMA strips has been widely tested in published research [ 16 , 17 , 18 ] and the first on-site Swiss application in carpentry used the FeSMA strips to the active strengthening of the RC slab [ 19 ]. Memory steel bars were embedded in a shotcrete layer [ 20 ] or applied as near-surface mounted reinforcement [ 21 , 22 , 23 , 24 ].…”

Section: Description Of Shape Memory Alloy Materialsmentioning

confidence: 99%

“…The restraining mechanism depends on the type of SMA and the strengthening technique [ 79 ]: For externally applied strips, the direct fastening system is used [ 80 ]. This method is based on the pre-drilling of the SMA strip to the concrete substrate and installation of the nails in the holes using powder-activated tools [ 19 , 79 ]; The NSM strips or bars can be inserted into the concrete grooves which are filled with cement-based grout [ 17 , 24 , 81 ]. The bars can be also fixed using end-anchors [ 21 , 82 ]; The NSM bars can be also embedded in the shotcrete layer [ 20 , 24 ].…”

Section: Comparison Of Prestressed Fesma and Cfrp Behaviormentioning

confidence: 99%

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Comparison of Prestressing Methods with CFRP and SMA Materials in Flexurally Strengthened RC Members

Rogowski

Kotynia

2022

Materials

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Over the years, prestressing concrete has become a well-known technique to improve the ultimate and serviceability state of RC members. Besides steel reinforcement, relatively new materials such as carbon fiber reinforced polymers (CFRP) or especially shape memory alloys (SMA) can be used to active strengthening. The main scope of this paper is to compare various prestressing methods using carbon composites and memory steel alloys. A description of SMA, shape memory effect, its utilization for prestressing, and CFRP materials are presented in the paper. Moreover, current state-of-the-art developments in the field of both materials, considering prestressing systems and available anchorage, material behavior, creep and stress relaxation, durability issues, thermal compatibility with concrete, and fire behavior, are described. A general revision of previous studies based on flexural strengthening using both materials is conducted and the selected results of these studies are briefly presented. The behavior of RC beams after strengthening with mentioned techniques is compared and discussed. Selected on-site applications are described to confirm the feasibility and practicality of the strengthening systems. Finally, the main advantages and disadvantages of CFRP and SMA materials for prestressing concrete structures are summarized and further recommendations for the future research are listed.

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Section: Description Of Shape Memory Alloy Materialsmentioning

confidence: 99%

Section: Comparison Of Prestressed Fesma and Cfrp Behaviormentioning

confidence: 99%

Comparison of Prestressing Methods with CFRP and SMA Materials in Flexurally Strengthened RC Members

Rogowski

Kotynia

2022

Materials

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“…Shape memory alloys (SMAs) are unique alloys that can recover their shapes caused by deformation through unloading or upon heating above a specific temperature. Because of the shape memory effect (SME) and superelasticity, SMAs are widely applied in the automotive, aerospace, robotic, civil engineering, and biomedical domains [1][2][3][4][5][6][7][8][9]. NiTibased SMAs (NiTi-SMAs) have played a leading role in the industry owing to their good SME.…”

Section: Introductionmentioning

confidence: 99%

Effect of Phase Changes on the Axial Modulus of an FeMnSi-Shape Memory Alloy

2021

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The axial modulus ESMA(κ) of FeMnSi-based shape memory alloys (FeMnSi-SMAs) is a parameter introduced in this study to characterize the relationship between stress and strain behavior at the early stage of tensile loading. ESMA(κ) can be used to correctly estimate and model the interaction forces between FeMnSi-SMAs and other materials. Unlike the conventional Young’s modulus, which is usually given at room temperature, the ESMA(κ) is evaluated at different temperatures and strongly depends on phase transformation and plastic deformation. This study investigated the evolution of ESMA(κ) during and after pre-straining as well as in the course of the activation processes. The effect of different factors (e.g., phase transformation and plastic deformation) on the magnitude of ESMA(κ) is discussed. The result shows that the ESMA(κ) can differ significantly during activation and thus needs to be modified when interaction forces between FeMnSi-SMAs and other substrates materials (e.g., concrete) must be modeled and evaluated.

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“…Siddiquee et al evaluated the seismic performance of concrete frame buildings reinforced with superelastic SMA rebar in terms of the collapse margin ratio, and the results are compared with that of a reinforced concrete structure with regular steel rebar only [ 9 ]. Schranz et al studied the effectiveness of two different prestressed strengthening methods for flexural behavior of concrete members using ribbed Fe-SMA bars [ 10 ]. Peng et al proposed a shape memory alloy cable-double friction pendulum bearing which combines a double friction pendulum bearing with superelastic SMA cables [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control

Zhan

Liu²,

Wang

et al. 2021

Materials

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The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling method. In this paper, an optimized neural network prediction model of shape memory alloy and its application for structural vibration control are discussed. The superelastic properties of austenitic SMA wires were tested by experiments. The material property test data were taken as the training samples of the BP neural network, and a prediction model optimized by the genetic algorithm was established. By using the improved genetic algorithm, the position and quantity of the SMA wires were optimized in a three-storey spatial structure, and the dynamic response analysis of the optimal arrangement was carried out. The results show that, compared with the unoptimized neural network prediction model of SMA, the optimized prediction model is in better agreement with the test curve and has higher stability, it can well reflect the effect of loading rate on the superelastic properties of SMA, and is a high precision rate-dependent dynamic prediction model. Moreover, the BP network constitutive model is simple to use and convenient for dynamic simulation analysis of an SMA passive control structure. The controlled structure with optimized SMA wires can inhibit the structural seismic responses more effectively. However, it is not the case that the more SMA wires, the better the shock absorption effect. When SMA wires exceed a certain number, the vibration reduction effect gradually decreases. Therefore, the seismic effect can be reduced economically and effectively only when the number and location of SMA wires are properly configured. When four SMA wires are arranged, the acceptable shock absorption effect is obtained, and the sum of the structural storey drift can be reduced by 44.51%.

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Strengthening and prestressing of bridge decks with ribbed iron-based shape memory alloy bars

Cited by 63 publications

References 34 publications

Comparison of Prestressing Methods with CFRP and SMA Materials in Flexurally Strengthened RC Members

Comparison of Prestressing Methods with CFRP and SMA Materials in Flexurally Strengthened RC Members

Effect of Phase Changes on the Axial Modulus of an FeMnSi-Shape Memory Alloy

Optimized Neural Network Prediction Model of Shape Memory Alloy and Its Application for Structural Vibration Control

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