The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this...The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.展开更多
With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.T...With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.To enhance the mechanical properties of CPB materials,fiber reinforcement technology has gradually gained attention,though challenges remain in predicting its performance.This study develops a hybrid model based on the adaptive equilibrium optimizer(adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB.Through systematic comparison with various other machine learning methods,results demonstrate that the proposed hybridmodel exhibits excellent predictive performance on the test set,achieving a coefficient of determination(R^(2))of 0.9675,root mean square error(RMSE)of 0.6084,and mean absolute error(MAE)of 0.4620.Input importance analysis reveals that cement-tailings ratio,curing time,and concentration are the three most critical factors affectingmaterial strength,with cement-tailings ratio showing a positive correlation with strength,concentrations above 70% significantly improvingmaterial strength,and curing periods beyond 28 days being essential for strength development.Fiber parameters contribute secondarily but notably to material strength,with fiber length exhibiting an optimal range of approximately 12 mm.This study not only provides a high-precision strength prediction model but also reveals the inherent correlations between various parameters and material performance,offering scientific basis for mixture optimization and engineering applications of fiber-reinforced CPB materials.展开更多
The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive st...The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.展开更多
In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SV...In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.展开更多
Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud servi...Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel values.After uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret,restore the cover image,or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.展开更多
In order to achieve a better understanding of failure behavior of cruciform specimen under different biaxial loading conditions,a three-dimensional finite element model is established with solid and interface elements...In order to achieve a better understanding of failure behavior of cruciform specimen under different biaxial loading conditions,a three-dimensional finite element model is established with solid and interface elements.Maximum stress criterion,two Hashin-type criteria and the new proposed criteria are used to predict the strength of plain woven textile composites when biaxial loading ratio equals 1.Compared with experimental data,only the new proposed criteria can reach reasonable results.The applicability of the new proposed criteria is also verified by predicting the tensile and compressive strength of cruciform specimen under different biaxial loading ratios.Moreover,the introduction of interface element makes it more intuitive to recognize delamination failure.The shape of the predicted delamination failure region in the interface layer is similar to that of the failure region in neighboring entity layers,but the area of delamination failure region is a little larger.展开更多
The research investigates ensemble machine learning techniques to forecast high-performance concrete(HPC)compressive strength through analysis of Gradient Boosting Machines(GBM)together with Random Forest(RF)and Deep ...The research investigates ensemble machine learning techniques to forecast high-performance concrete(HPC)compressive strength through analysis of Gradient Boosting Machines(GBM)together with Random Forest(RF)and Deep Neural Network(DNN)performances.Previous experiment data served as model inputs for the machine learning systems that comprised cement,fly ash,blast furnace slag,water,superplasticizer,coarse aggregate,and fine aggregate for HPC compressive strength prediction.The research study utilizes input parameters and direct bypassing of dimensionality reduction to evaluate the performance of models that capture intricate nonlinear patterns from concrete compressive strength data.RF produced the most accurate results during training by establishing 0.9650 R^(2) measurements and 0.0798 RMSE indicators,thus demonstrating exceptional accuracy at a minimal error level.In testing,RF maintained its lead with an R^(2) of 0.9399,followed closely by GBM,while DNN showed slightly higher error rates.A comprehensive ranking analysis across multiple statistical metrics highlighted RF as the most dependable concrete compressive strength prediction model.Further,Regression Error Characteristic(REC)curves visually assessed model performance relative to error tolerance,revealing RF and GBM’s reliable accuracy across different thresholds.A Graphical User Interface(GUI)with user-oriented features connected to the prediction models was created for smooth system usage.The results indicate that RF provides accurate predictions for concrete compressive strength because of the effectiveness of ML models,according to this study.Predictions of tensile strength,modulus of elasticity,and fracture energy parameters in concrete materials become possible when categorized based on their compressive strength values.This approach significantly enhances structural analysis by reducing both cost and time requirements.展开更多
Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e....Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.展开更多
Sprayed concrete lining is a commonly employed support measure in tunnel engineering,which plays an important role in construction safety.Compressive strength is a key performance indicator of sprayed concrete lining,...Sprayed concrete lining is a commonly employed support measure in tunnel engineering,which plays an important role in construction safety.Compressive strength is a key performance indicator of sprayed concrete lining,and the traditional measuring method is time-consuming and laborious.This paper proposes various hybrid machine learning algorithms to accomplish the advanced prediction of compressive strength of sprayed concrete lining based on the mixture design.Two hundred and five sets of experimental data were collected from a water conveyance tunnel in northwestern China for model construction,and each set of data was made up of six basic input variables(i.e.,water,cement,mineral powder,superplasticizer,coarse aggregate,and fine aggregate)and one output variable(i.e.,compressive strength).In order to eliminate the correlation between input variables,a new composite indicator(i.e.,the water-binder ratio)was introduced to achieve dimensionality reduction.After that,four hybrid models in total were built,namely BPNN-QPSO,SVR-QPSO,ELM-QPSO,and RF-QPSO,where the hyper-parameters of BPNN,SVR,ELM,and RF were auto-tuned by QPSO.Engineering application results indicated that RF-QPSO achieved the lowest mean absolute percentage error(MAPE)of 3.47% and root mean square error(RMSE)of 1.30 and the highest determination coefficient(R^(2))of 0.93 in the four hybrid models.Moreover,RFQPSO had the shortest running time of 0.15 s,followed by SVR-QPSO(0.18 s),ELM-QPSO(1.19 s),and BPNN-QPSO(1.58 s).Compared with BPNN-QPSO,SVR-QPSO,and ELM-QPSO,RF-QPSO performed the most superior performance in terms of both prediction accuracy and running speed.Finally,the importance of input variables on the model performance was quantitatively evaluated,further enhancing the interpretability of RF-QPSO.展开更多
In recent building practice,rapid construction is one of the principal requisites.Furthermore,in designing concrete structures,compressive strength is the most significant of all parameters.While 3-d and 7-d compressi...In recent building practice,rapid construction is one of the principal requisites.Furthermore,in designing concrete structures,compressive strength is the most significant of all parameters.While 3-d and 7-d compressive strength reflects the strengths at early phases,the ultimate strength is paramount.An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate(EVA)at the later phases.Kolmogorov-Smirnov(KS)goodness-of-fit test was used to examine distribution of the data.The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28,56,90,120,210,and 365 d.An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit.Based on the results of KS goodness-of-fit test,it was concluded that KS test statistics value(D)in each case was lower than the critical value 0.521 for a significance level of 0.05,which demonstrated that the data was normally distributed.Based on the results of compressive strength test,it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16%concentration.Furthermore,it was concluded that predicted compressive strength values lies within a 6%difference from the actual strength values for all the mixes,which indicates the practicability of the regression equations.This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.展开更多
Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding o...Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.展开更多
基金the National Natural Science Foundation of China(Nos.51608380 and 51538009)the Key Innovation Team Program of the Innovation Talents Promotion Plan by Ministry of Science and Technology of China(No.2016RA4059)the Specific Consultant Research Project of Shanghai Tunnel Engineering Company Ltd.(No.STEC/KJB/XMGL/0130),China。
文摘The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.
基金funded by the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073)supported by China Scholarship Council with the grant number of 202006370006.
文摘With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.To enhance the mechanical properties of CPB materials,fiber reinforcement technology has gradually gained attention,though challenges remain in predicting its performance.This study develops a hybrid model based on the adaptive equilibrium optimizer(adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB.Through systematic comparison with various other machine learning methods,results demonstrate that the proposed hybridmodel exhibits excellent predictive performance on the test set,achieving a coefficient of determination(R^(2))of 0.9675,root mean square error(RMSE)of 0.6084,and mean absolute error(MAE)of 0.4620.Input importance analysis reveals that cement-tailings ratio,curing time,and concentration are the three most critical factors affectingmaterial strength,with cement-tailings ratio showing a positive correlation with strength,concentrations above 70% significantly improvingmaterial strength,and curing periods beyond 28 days being essential for strength development.Fiber parameters contribute secondarily but notably to material strength,with fiber length exhibiting an optimal range of approximately 12 mm.This study not only provides a high-precision strength prediction model but also reveals the inherent correlations between various parameters and material performance,offering scientific basis for mixture optimization and engineering applications of fiber-reinforced CPB materials.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503)。
文摘The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.
基金Funded by Natioanl Natural Science Foundation of Chin a(Nos.2012BAJ11B00,41301588,41471339,41571514)the Center for Materials Research and Analysis,Wuhan University of Technology
文摘In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.
基金the Ministry of Science and Technology of Taiwan,Grant Number MOST 110-2221-E-507-003.
文摘Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel values.After uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret,restore the cover image,or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.
基金supported by the National Natural Science Foundation of China(No.51205190)the Jiangsu Province Key Laboratory of Aerospace Power System(No.NJ20140019)
文摘In order to achieve a better understanding of failure behavior of cruciform specimen under different biaxial loading conditions,a three-dimensional finite element model is established with solid and interface elements.Maximum stress criterion,two Hashin-type criteria and the new proposed criteria are used to predict the strength of plain woven textile composites when biaxial loading ratio equals 1.Compared with experimental data,only the new proposed criteria can reach reasonable results.The applicability of the new proposed criteria is also verified by predicting the tensile and compressive strength of cruciform specimen under different biaxial loading ratios.Moreover,the introduction of interface element makes it more intuitive to recognize delamination failure.The shape of the predicted delamination failure region in the interface layer is similar to that of the failure region in neighboring entity layers,but the area of delamination failure region is a little larger.
文摘The research investigates ensemble machine learning techniques to forecast high-performance concrete(HPC)compressive strength through analysis of Gradient Boosting Machines(GBM)together with Random Forest(RF)and Deep Neural Network(DNN)performances.Previous experiment data served as model inputs for the machine learning systems that comprised cement,fly ash,blast furnace slag,water,superplasticizer,coarse aggregate,and fine aggregate for HPC compressive strength prediction.The research study utilizes input parameters and direct bypassing of dimensionality reduction to evaluate the performance of models that capture intricate nonlinear patterns from concrete compressive strength data.RF produced the most accurate results during training by establishing 0.9650 R^(2) measurements and 0.0798 RMSE indicators,thus demonstrating exceptional accuracy at a minimal error level.In testing,RF maintained its lead with an R^(2) of 0.9399,followed closely by GBM,while DNN showed slightly higher error rates.A comprehensive ranking analysis across multiple statistical metrics highlighted RF as the most dependable concrete compressive strength prediction model.Further,Regression Error Characteristic(REC)curves visually assessed model performance relative to error tolerance,revealing RF and GBM’s reliable accuracy across different thresholds.A Graphical User Interface(GUI)with user-oriented features connected to the prediction models was created for smooth system usage.The results indicate that RF provides accurate predictions for concrete compressive strength because of the effectiveness of ML models,according to this study.Predictions of tensile strength,modulus of elasticity,and fracture energy parameters in concrete materials become possible when categorized based on their compressive strength values.This approach significantly enhances structural analysis by reducing both cost and time requirements.
文摘Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.
基金supported by the National Natural Science Foundation of China[Grant numbers 41941018,52074258,42177140,and 41807250]the Key Research and Development Project of Hubei Province[Grant number 2021BCA133].
文摘Sprayed concrete lining is a commonly employed support measure in tunnel engineering,which plays an important role in construction safety.Compressive strength is a key performance indicator of sprayed concrete lining,and the traditional measuring method is time-consuming and laborious.This paper proposes various hybrid machine learning algorithms to accomplish the advanced prediction of compressive strength of sprayed concrete lining based on the mixture design.Two hundred and five sets of experimental data were collected from a water conveyance tunnel in northwestern China for model construction,and each set of data was made up of six basic input variables(i.e.,water,cement,mineral powder,superplasticizer,coarse aggregate,and fine aggregate)and one output variable(i.e.,compressive strength).In order to eliminate the correlation between input variables,a new composite indicator(i.e.,the water-binder ratio)was introduced to achieve dimensionality reduction.After that,four hybrid models in total were built,namely BPNN-QPSO,SVR-QPSO,ELM-QPSO,and RF-QPSO,where the hyper-parameters of BPNN,SVR,ELM,and RF were auto-tuned by QPSO.Engineering application results indicated that RF-QPSO achieved the lowest mean absolute percentage error(MAPE)of 3.47% and root mean square error(RMSE)of 1.30 and the highest determination coefficient(R^(2))of 0.93 in the four hybrid models.Moreover,RFQPSO had the shortest running time of 0.15 s,followed by SVR-QPSO(0.18 s),ELM-QPSO(1.19 s),and BPNN-QPSO(1.58 s).Compared with BPNN-QPSO,SVR-QPSO,and ELM-QPSO,RF-QPSO performed the most superior performance in terms of both prediction accuracy and running speed.Finally,the importance of input variables on the model performance was quantitatively evaluated,further enhancing the interpretability of RF-QPSO.
文摘In recent building practice,rapid construction is one of the principal requisites.Furthermore,in designing concrete structures,compressive strength is the most significant of all parameters.While 3-d and 7-d compressive strength reflects the strengths at early phases,the ultimate strength is paramount.An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate(EVA)at the later phases.Kolmogorov-Smirnov(KS)goodness-of-fit test was used to examine distribution of the data.The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28,56,90,120,210,and 365 d.An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit.Based on the results of KS goodness-of-fit test,it was concluded that KS test statistics value(D)in each case was lower than the critical value 0.521 for a significance level of 0.05,which demonstrated that the data was normally distributed.Based on the results of compressive strength test,it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16%concentration.Furthermore,it was concluded that predicted compressive strength values lies within a 6%difference from the actual strength values for all the mixes,which indicates the practicability of the regression equations.This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.
文摘Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.
