Polymer Concrete for Bridge Deck Closure Joints in Accelerated Bridge Construction

Mantawy, Islam M.; Chennareddy, Rahulreddy; Genedy, Moneeb; Taha, Mahmoud Reda

doi:10.3390/infrastructures4020031

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…This is because both specimens start behaving nonlinearly from the 400 N load due to the material nonlinear behavior of the polymer concrete mix. Nonlinear behavior of polymer concrete beams has been widely reported in the literature [ 70 , 71 , 72 ]. This material non-linearity causes a shift in the neutral axis position and will cause the middle textile layer of TRPC to bear load.…”

Section: Resultsmentioning

confidence: 99%

Flexural Behavior of a Novel Textile-Reinforced Polymer Concrete

et al. 2022

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Textile reinforced concrete (TRC) has gained attention from the construction industry due to its light weight, high tensile strength, design flexibility, corrosion resistance, and remarkably long service life. Some structural applications that utilize TRC components include precast panels, structural repair, waterproofing elements, and façades. TRC is produced by incorporating textile fabrics into thin cementitious concrete panels. Premature debonding between the textile fabric and concrete due to improper cementitious matrix impregnation of the fibers was identified as a failure-governing mechanism. To overcome this performance limitation, in this study, a novel type of TRC is proposed by replacing the cement binder with a polymer resin to produce textile reinforced polymer concrete (TRPC). The new TRPC is created using a fine-graded aggregate, methyl methacrylate polymer resin, and basalt fiber textile fabric. Four different specimen configurations were manufactured by embedding 0, 1, 2, and 3 textile layers in concrete. Flexural performance was analyzed and compared with reference TRC specimens with similar compressive strength and reinforcement configurations. Furthermore, the crack pattern intensity was determined using an image processing technique to quantify the ductility of TRPC compared with conventional TRC. The new TRPC improved the moment capacity compared with TRC by 51%, 58%, 59%, and 158%, the deflection at peak load by 858%, 857%, 3264%, and 3803%, and the toughness by 1909%, 3844%, 2781%, and 4355% for 0, 1, 2, and 3 textile layers, respectively. TRPC showed significantly improved flexural capacity, superior ductility, and substantial plasticity compared with TRC.

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Section: Resultsmentioning

confidence: 99%

Flexural Behavior of a Novel Textile-Reinforced Polymer Concrete

et al. 2022

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“…Although there has been some research on structural applications of PC ( 21 – 24 ), research on the bond behavior of PC is limited. That limited research displayed the superior bond strength of PMMA-PC with deformed rebar.…”

Section: Research Significancementioning

confidence: 99%

Interfacial Shear Bond Strength between Steel H-Piles and Polymer Concrete Jackets

Abdulazeez

Brown

ElGawady

2020

Transportation Research Record

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Steel H-piles have been used widely in bridge construction throughout the U.S. because of their relatively large load-carrying capacity while occupying a small area. However, many H-piles suffer from corrosion, which may lead to abrupt collapse. A cost-effective repair technique, including encasing the corroded region of the steel pile into a concrete jacket, which acts as an alternative load path for the applied axial load, has been used by several state Departments of Transportation. Methyl methacrylate polymer concrete (MMA-PC) is a type of concrete that is commonly used as a repair material. However, there is limited research on the assessment of bond strength between MMA-PC and steel elements. This paper investigates experimentally the bond behavior of seven full-scale steel H-piles encased in concrete jackets. The jackets were cast using either MMA-PC or Portland cement concrete (CC). Different embedment lengths of 63.5 mm (2.5 in.), 127 mm (5 in.), and 190.5 mm (7.5 in.) were used for the MMA-PC and one embedment length of 254 mm (10 in.) was used for the CC jacket. Cylindrical and prismatic jacket configurations were used and tested using push-out. The experimental results revealed that using the MMA-PC jacket was more effective compared with the CC jacket in relation to the load-carrying capacity. For design purposes, a shear bond stress of 2.96 MPa [0.43 kips per square inch (ksi)] can be used for MMA-PC jackets having an embedment length of at least 127 mm (5 in.) whereas a value of 0.83 MPa (0.12 ksi) can be used for CC.

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“…The mechanical properties of UHPC with various mix designs and additives have been the focus of numerous recent investigations. Researchers have shown interest in using eco-friendly supplementary cementitious materials (SCMs) such as ground granulated blast slag furnaces (GGBFS) and fly ash (FA) in concrete [7][8][9][10][11][12]. This is due to the fact that cement is a major contributor to CO 2 emissions, accounting for about 8% of global CO 2 emissions [13].…”

Section: Introductionmentioning

confidence: 99%

Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages

Safieh,

Hawileh,

Assad

et al. 2024

Infrastructures

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Ultra High-Performance Concrete (UHPC) has shown extraordinary performance in terms of strength and durability. However, having a cost-effective and sustainable UHPC mix design is a challenge in the construction sector. This study aims on building a predictable model that can help in determining the compressive strength of UHPC. The research focuses on applying multiple machine learning (ML) models and evaluating their performance in predicting the strength prediction of UHPC. Two reliable metrics are used to evaluate the performance of the model which are the coefficient of determination (R2) and mean squared error (MSE). The parameters that are affecting the compressive strength of UHPC are fly ash percentage levels (FA%), superplasticizer content, water to binder ratio (w/b), and curing period. A total of 54 ML models were used, consisting of Linear Regression, Support Vector Machines (SVM), Neural Networks, and Random forests algorithms. Among these models, Random Forest proved to be the most effective in capturing the relationships in UHPC’s behaviour with an R squared score of 0.8857. The Random Forest ML model is also used in this paper to conduct a parametric study that will help in obtaining the compressive strength of UHPC with higher content of FA%, which is not sufficiently studied in the literature.

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Polymer Concrete for Bridge Deck Closure Joints in Accelerated Bridge Construction

Cited by 10 publications

References 9 publications

Flexural Behavior of a Novel Textile-Reinforced Polymer Concrete

Flexural Behavior of a Novel Textile-Reinforced Polymer Concrete

Interfacial Shear Bond Strength between Steel H-Piles and Polymer Concrete Jackets

Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages

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