Deep learning-based prediction for time-dependent chloride penetration in concrete exposed to coastal environment

Wu, Lingjie; Wang, Weiqiang; Jiang, Chenchi

doi:10.1016/j.heliyon.2023.e16869

Cited by 3 publications

(1 citation statement)

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“…With the application of machine learning algorithms, research on the FRP-to-concrete interface's performance is no longer limited to experimentation. Employing data-driven methodologies to address these complex prediction challenges is a pragmatic approach [14,15]. Researchers have advocated for the utilization of artificial neural network (ANN) models to forecast the strength of interfacial bonds, yielding superior predictive outcomes [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

Wang,

Ye,

Liu

et al. 2024

Applied Sciences

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Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

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