research-on-transmission-line-personal-protective-equipment-detection

Research on Transmission Line Personal Protective Equipment Detection Algorithm Based on Improved YOLOv11

In intelligent transmission line inspection, edge computing devices face the critical challenge of balancing real-time performance with detection accuracy ...

Hebei University of Technology

The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio () and the tensile strength of the FRP () were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.

Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation.

In this study, the chloride ion (Cl) binding rate of sulfoaluminate cement (SAC) mortars containing different mineral admixtures was investigated. This is essential to improve the durability of concrete structures in high Cl environments, especially where they are susceptible to Cl attack such as coastlines and marine structures. The effects of Cl concentration, curing age, and the type and amount of mineral admixture on the Cl binding rate of SAC mortars were analyzed. It was found that the content of water-soluble Cl in SAC mortars decreased with the increase of curing age, while the Cl binding ratio increased accordingly, indicating that its resistance to internal Cl permeation increased. The addition of fly ash (FA) and ground granulated blast furnace slag (GGBS) can significantly improve the Cl binding rate of SAC mortars, and the Cl binding rate increases to 46.6% with 20% of FA and 38.7% with 40% of GGBS. The effects of mineral admixtures on the microstructure and phase composition of SAC mortars were further investigated by X-ray diffraction (XRD) analysis. The results showed that the addition of FA and GGBS promoted the formation of C-S-H (calcium silicate hydrate) gels and improved the resistance of SAC mortars to Cl penetration. On the other hand, the excessive addition of silica fume (SF) decreased the Cl binding rate, whereas a moderate amount of limestone powder (LP) improved the Cl binding rate. The study of the Cl binding rate of SAC mortars can help to evaluate their resistance to Cl erosion in real projects, thus guiding the optimization of concrete formulations and the improvement of durability.

Article	Year
Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation. Materials (Basel, Switzerland)\| PubMed ID: 35888438	2022
Study on the chloride ion binding rate of sulfoaluminate cement mortars containing different mineral admixtures. Scientific reports\| PubMed ID: 39414904	2024

Pang Chen

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