Compressive behavior and microstructure of concrete mixed with natural seawater and sea sand

Noncorrosive reinforcement materials facilitate producing structural concrete with seawater and sea sand.This study investigated the properties of seawater and sea sand concrete(SSC),considering the curing age(3,7,14,21,28,60,and 150 d)and strength grade(C30,C40,and C60).The compressive behavior of SSC was obtained by compressive tests and digital image correction(DIC)technique.Scanning electron microscope(SEM)and X-ray powder diffraction(XRD)methods were applied to understand the microstructure and hydration products of cement in SSC.Results revealed a 30%decrease in compressive strength for C30 and C40 SSC from 60 to 150 d,and a less than 5%decrease for C60 from 28 to 150 d.DIC results revealed significant cracking and crushing from 80%to 100%of compressive strength.SEM images showed a more compact microstructure in higher strength SSC.XRD patterns identified Friedel’s salt phase due to the chlorides brought by seawater and sea sand.The findings in this study can provide more insights into the microstructure of SSC along with its short-and long-term compressive behavior.

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.

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.