Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for pre...Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.展开更多
A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the ...A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of pavements.In this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements.The performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)algorithms.These models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR models.Therefore,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.展开更多
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...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.展开更多
In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand....In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted results.However,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs.Thus,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.展开更多
Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)models.The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST chang...Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)models.The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes.This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon.Artificial neural network(ANN)and gene expression programming(GEP)were employed to develop LST prediction models.Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models.The extracted data of LULC indices,such as normal difference vegetation index(NDVI),normal difference built-up index(NDBI),urban index(UI),and normal difference water index(NDWI),were utilized as attributes to model LST.The results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,respectively.The GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation,which can be conveniently used to test new data regarding the thematic area.The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST.展开更多
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 tempe...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.展开更多
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-02-02385).
文摘Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.
基金We acknowledge the support provided by the University of Transport Technology.
文摘A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of pavements.In this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements.The performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)algorithms.These models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR models.Therefore,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.
基金the financial aid from the National Natural Science Founda-tion of China(12072192,U1831105)the Natural Science Foun-dation of Shanghai(20ZR1429500).
文摘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.
文摘In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted results.However,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs.Thus,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.
基金supported by Korea Agency for Infrastructure Technology Advancement:[Grant Number 21CFRP-C163381-01].
文摘Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)models.The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes.This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon.Artificial neural network(ANN)and gene expression programming(GEP)were employed to develop LST prediction models.Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models.The extracted data of LULC indices,such as normal difference vegetation index(NDVI),normal difference built-up index(NDBI),urban index(UI),and normal difference water index(NDWI),were utilized as attributes to model LST.The results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,respectively.The GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation,which can be conveniently used to test new data regarding the thematic area.The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST.
文摘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.
