Behaviour of concrete filled glass fibre-reinforced polymer tubes under static and flexural fatigue loading

Aslani, Farhad; Gunawardena, Yasoja; Dehghani, Ayoub

doi:10.1016/j.conbuildmat.2019.03.321

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…FGB technology present great benefits on monitoring the quality of concrete [ 88 , 89 , 90 ]. Sensing technologies also invite digital technologies and artificial intelligence into the construction industry to predict the mechanical properties under static and flexural fatigue loading [ 91 , 92 , 93 ]. Forty references related to motivations were detected in fourteen interviews and assigned to eight nodes.…”

Section: Qualitative Data Collection and Analysismentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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Sensing technologies present great improvements in construction performance including the safety, productivity, and quality. However, the corresponding applications in real projects are far behind compared with the academically research. This research aims to discover dominate influence factors in the sensing technologies adoption and ultimately develop a governance framework facilitating adoption processes. The framework is dedicated on general sensing technologies rather than single sensor in previous framework studies. To begin with, the influence factors of sensing technologies and other similar emerging technologies are summarised through a review. Then, a mixed methods design was employed to collect quantitative data through an online survey, and qualitative data through semi-structured interviews. Findings of the quantitative method reveal that the most widely implemented sensing technologies are GPS and visual sensing technology, but they’re still not adopted by all construction companies. Partial Least Squares Structural Equation Modelling reveals that supplier characteristics have the highest effect in all influence factors. Qualitative method was adopted to investigate perceptions of construction stakeholders on the major decision-making considerations in the adoption process. Ultimately, a triangulation analysis of findings from the literature review, online survey and interviews resulted in the governance framework development. The overarching contribution of this research focus on the general adoption of sensing technologies rather than the adoption of a specific sensor. Therefore, the governance framework can assist with the decision-making process of any sensing technology adoption in construction.

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Section: Qualitative Data Collection and Analysismentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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“…This paper presents one part of an experimental campaign conducted by the authors to study the performance of pultruded concrete-filled GFRP tubes under repeated loading-unloading cycles in compression and flexure. 62…”

Section: Significance Of Researchmentioning

confidence: 99%

Experimental investigation of the behavior of concrete‐filled high‐strength glass fiber‐reinforced polymer tubes under static and cyclic axial compression

Aslani

Deghani

Gunawardena

2020

Structural Concrete

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This experimental study investigated the load–displacement behavior, ultimate strength, and failure modes of hollow and concrete‐filled high‐strength glass fiber‐reinforced polymer (GFRP) tubes under static and cyclic axial compression. For this purpose, pultruded GFRP tubes were used which contained fibers oriented at 0°, +45°, and −45° to the longitudinal axes of the tubes. Self‐compacting concrete (SCC) was used for in‐filling the hollow GFRP tubes. The main parameters considered in the test program were the tube cross‐section, column length, and cyclic loading range. A total of 28 specimens of lengths equal to either 0.5 or 1.5 m were tested under static and cyclic axial compressive loading. The experimentally obtained load–displacement and load‐axial/hoop strain behaviors are discussed in this paper. It was verified that in‐filling the GFRP tubes with concrete can enhance their axial strength and stiffness, though it is not able to prevent the brittle nature of the failure. Hollow GFRP tubes displayed linear‐elastic behavior with a constant axial stiffness up to failure under both static and cyclic loading, while the concrete‐filled specimens exhibited a bilinear load–displacement behavior with the change in stiffness occurring at the point at which the GFRP tube starts to act as a confining jacket. Under cyclic loading, the concrete‐filled specimens suffered degradation in axial stiffness with the rate of deterioration decreasing with increasing number of cycles. However, a consistent relationship between the cyclic load range magnitude and number of cycles to failure could not be found using the experimentally obtained data.

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“…Besides, solid waste materials, such as waste glass and recycled aggregate, have the potential to be applied in HSC to overcome the strength shortcomings of the waste itself [24][25][26][27][28][29][30][31]. Therefore, HSC incorporated with solid wastes has the benefits of both strength enhancement and sustainable prospect [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

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Behaviour of concrete filled glass fibre-reinforced polymer tubes under static and flexural fatigue loading

Cited by 24 publications

References 27 publications

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Experimental investigation of the behavior of concrete‐filled high‐strength glass fiber‐reinforced polymer tubes under static and cyclic axial compression

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

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