Generational Advancements in the Transverse Shear Strength Retention of Glass Fiber-Reinforced Polymer Bars in Alkaline and Acidic Environments

被引:3
|
作者
Al-Zahrani, Mesfer M. [1 ,2 ]
机构
[1] King Fahd Univ Petr & Minerals, Civil & Environm Engn Dept, Dhahran 31261, Saudi Arabia
[2] King Fahd Univ Petr & Minerals, Interdisciplinary Res Ctr Construct & Bldg Mat IRC, Dhahran 31261, Saudi Arabia
关键词
glass fiber-reinforced polymer (GFRP) bars; long-term performance; accelerated aging; artificial neural network; linear regression; COMPRESSIVE STRENGTH; CARBON-FIBER; GFRP; PERFORMANCE; DURABILITY; PREDICTION; BEHAVIOR; BASALT;
D O I
10.3390/polym16192712
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
In this study, the transverse shear strength (TSS) retention of two types of new-generation glass fiber-reinforced polymer (GFRP) bars, namely ribbed (RB) and sand-coated (SC) bars, was investigated under alkaline, acidic, and marine conditions in both high-temperature and laboratory environments for up to one year. The ribbed GFRP bars exhibited no notable reduction in strength under ambient conditions after 12 months, but under high-temperature conditions (60 degrees C), they showed TSS reductions of 10.6%, 9.7%, 11.1%, and 10.9% for exposure solutions E1, E2, E3, and E4, respectively. The sand-coated GFRP bars showed slight strength reductions under ambient conditions and moderate reductions under high-temperature conditions (60 degrees C), with TSS reductions of 22.5%, 29.0%, 13.0%, and 13.7% for the same solutions, highlighting the detrimental effect of high temperatures on the degradation of the resin matrix. Comparative analyses of older-generation ribbed (RB-O1 and RB-O2) and sand-coated (SC-O) GFRP bars exposed to similar conditioning solutions for the same duration were also performed. In addition, linear regression and artificial neural network (ANN) models were developed to predict strength retention. Models developed using linear regression and ANNs achieved coefficients of determination (R2) of 0.69 and 0.94, respectively, indicating that the ANN model is a more robust tool for predicting the TSS of GFRP bars than is the conventional linear regression model.
引用
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页数:24
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