Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models

The long-term durability of glass fiber reinforced polymer(GFRP)bars in harsh alkaline environments is of great importance in engineering,which is reflected by the environmental reduction factor in vari-ous structural codes.The calculation of this factor requires robust models to predict the residual tensile strength of GFRP bars.Therefore,three robust metaheuristic algorithms,namely particle swarm optimiza-tion(PSO),genetic algorithm(GA),and support vector machine(SVM),were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system(ANFIS)in order to obtain more accurate prediction model.Various optimized models were developed to predict the tensile strength retention(TSR)of degraded GFRP rebars in typical alkaline environments(e.g.,seawater sea sand concrete(SWSSC)environment in this study).The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging.A to-tal number of 715 experimental laboratory samples were collected in a form of extensive database to be trained.K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal folds.In order to analyze the efficiency of the metaheuristic algorithms,multiple statistical tests were performed.It was concluded that the ANFIS-SVM-based model is robust and accu-rate in predicting the TSR of conditioned GFRP bars.In the meantime,the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete en-vironment.The sensitivity analysis revealed GFRP bar size,volume fraction of fibers,and pH of solution were the most influential parameters of TSR.

3D multicore-shell CoSn nanoboxes encapsulated in porous carbon as anode for lithium-ion batteries

Due to its high theoretical capacity and appropriate potential platform,tin-based alloy materials are expected to be a competitive candidate for the next-generation high performance anodes of lithium-ion batteries.Nevertheless,the immense volume change during the lithium-ion insert process leads to severe disadvantages of structural damage and capacity fade,which limits its practical application.In this work,a three-dimensional(3 D)multicore-shell hollow nanobox encapsulated by carbon layer is obtained via a three-step method of hydrothermal reaction,annealing and alkali etching.During the electrochemical reactions,the CoSn@void@C nanoboxes provide internal space to compensate the volumetric change upon the lithiation of Sn,while the inactive component of Co acts as chemical buffers to withstand the anisotropic expansion of nanoparticles.Owing to the above-mentioned advantages,the elaborated anode delivers an excellent capacity of 788.2 m Ah/g at 100 m A/g after 100 cycles and considerable capacity retention of 519.2 mAh/g even at a high current density of 1 A/g after 300 cycles.The superior stability and high performance indicate its capability as promising anodes for lithium-ion batteries.

Durability evaluation of GFRP rebars in harsh alkaline environment using optimized tree-based random forest model

GFRP bars reinforced in submerged or moist seawater and ocean concrete is subjected to highly alkaline conditions.While investigating the durability of GFRP bars in alkaline environment,the effect of surrounding temperature and conditioning duration on tensile strength retention(TSR)of GFRP bars is well investigated with laboratory aging of GFRP bars.However,the role of variable bar size and volume fraction of fiber have been poorly investigated.Additionally,various structural codes recommend the use of an additional environmental reduction factor to accurately reflect the long-term performance of GFRP bars in harsh environments.This study presents the development of Random Forest(RF)regression model to predict the TSR of laboratory conditioned bars in alkaline environment based on a reliable database comprising 772 tested specimens.RF model was optimized,trained,and validated using variety of statistical checks available in the literature.The developed RF model was used for the sensitivity and parametric analysis.Moreover,the formulated RF model was used for studying the long-term performance of GFRP rebars in the alkaline concrete environment.The sensitivity analysis exhibited that temperature and pH are among the most influential attributes in TSR,followed by volume fraction of fibers,duration of conditioning,and diameter of the bars,respectively.The bars with larger diameter and high-volume fraction of fibers are less susceptible to degradation in contrast to the small diameter bars and relatively low fiber’s volume fraction.Also,the long-term performance revealed that the existing recommendations by various codes regarding environmental reduction factors are conservative and therefore needs revision accordingly.