Innovative model for accurate prediction of the transfer length of prestressing strands based on artificial neural networks: Case study

Alhassan, Mohammad A.; Ababneh, Ayman; Betoush, Nour

doi:10.1016/j.cscm.2019.e00312

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…The final configuration and parameters of the designed ANNs are summarized in Table 3. In the definition of the ANN topology, a trial-and-error process is required at different stages ( [13,17,[35][36][37][38]): (i) in the definition of neurons in the hidden layers, (ii) in the testing and learning process, and (iii) in the selection of the random weights. Those trial-and-error procedures make it possible to establish a suitable and stable network.…”

Section: Implementation Of the Anns: Design And Selection Of The Ann mentioning

confidence: 99%

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

2020

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In the built environment, one of the main concerns during the design stage is the selection of adequate structural materials and elements. A rational and sensible design of both materials and elements results not only in economic benefits and computing time reduction, but also in minimizing the environmental impact. Nowadays, Artificial Neural Networks (ANNs) are showing their potential as design tools. In this research, ANNs are used in order to foster the implementation of efficient tools to be used during the early stages of structural design. The proposed networks are applied to a dry precast concrete connection, which has been modelled by means of the Finite Element Method (FEM). The parameters are: strength of concrete and screws, diameter of screws, plate thickness, and the posttensioning load. The ANN input data are the parameters and nodal stresses obtained from the FEM models. A multilayer perceptron combined with a backpropagation algorithm is used in the ANN architecture, and a hyperbolic tangent function is applied as an activation function. Comparing the obtained predicted stresses to those of the FEM analyses, the difference is less than 9.16%. Those results validate their use as an efficient structural design tool. The main advantage of the proposed ANNs is that they can be easily and effectively adapted to different connection parameters. In addition, their use could be applied both in precast or cast in situ concrete connection design.

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Section: Implementation Of the Anns: Design And Selection Of The Ann mentioning

confidence: 99%

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

2020

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“…The mechanism is similar to pre-tensioned prestressed tendons, but there are still some differences. On the one hand, post-tensioned rebars are built in a dense arrangement, thus making the Hall effect more significant [25]. On the other hand, the corrosion of prestressed beams progresses slowly, which is different from the fast expansion of pre-tensioned rebars.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Secondary Transfer Performances of Prestress after Anchoring Failure of Steel Wire Strands

Yang,

Zhang

et al. 2023

Metals

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To understand the secondary transfer performances of residual prestress after the anchoring failure of end-anchored steel wire strands due to corrosion fracture, six steel wire strand components of post-tensioning prestress were designed and fabricated. One-side fast corrosion was applied to the steel wire strand components using the electrochemical method until anchoring failure was reached. The sphere of influence, stress changes, and the retraction and swelling effect of broken beams after failure were investigated. The influences of factors such as concrete strength, stirrup area, and the length of the component on the secondary transfer length of residual prestress were discussed. Based on the deformation relationship between prestressed steel wire strands and concrete in the stress transfer zone, a stress equation was established and solved through a bond constitutive model. A prediction model of the effective stress transfer length of prestressed steel wire strand after failure was proposed. The results demonstrated that residual prestress can have a secondary transfer after the corrosion fracture of end-anchored steel wire strands, but some effective prestress may be lost. Moreover, the loss of prestress is inversely proportional to concrete compressive strength. When the specimens are relatively short, the prestress loss increases significantly. Concrete strength has significant influences on the length of secondary transfer. The proposed simplified calculation method of the secondary transfer length of residual prestress has a relatively high accuracy, with an average error of 2.9% and a maximum error of 5.2%.

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State-of-the-Art of Artificial Intelligence Methods in Structural Health Monitoring

Putra

2022

Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering

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Innovative model for accurate prediction of the transfer length of prestressing strands based on artificial neural networks: Case study

Cited by 7 publications

References 15 publications

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

An Experimental Study on Secondary Transfer Performances of Prestress after Anchoring Failure of Steel Wire Strands

State-of-the-Art of Artificial Intelligence Methods in Structural Health Monitoring

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