Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timec...Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.展开更多
The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fractu...The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy,as the process remains time-intensive and costly.Therefore,machine learning techniques have emerged as powerful alternatives.This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC.For this purpose,500 data points,including 8 input parameters that affect the fracture energy of FRC,are collected fromthree-point bending tests and employed to train and evaluate themachine learning techniques.The findings showed that Gaussian process regression(GPR)outperforms all other models in terms of predictive accuracy,achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation.It is then followed by support vector regression(SVR)and extreme gradient boosting regression(XGBR),whereas K-nearest neighbours(KNN)and random forest regression(RFR)show the weakest predictions.The superiority of GPR is further reinforced in a 5-fold cross-validation,where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance.Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy,cementing its claim.The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction,whereas the glass fiber dominates energy absorption amongst the other types of fibers.In addition,increasing the water-to-cement(W/C)ratio from 0.30 to 0.50 yields a significant improvement in fracture energy,which aligns well with the machine learning predictions.Similarly,loading rate positively correlates with fracture energy,highlighting the strain-rate sensitivity of FRC.This work is the missing link to integrate experimental fracture mechanics and computational intelligence,optimally and reasonably predicting and refining the fracture energy of FRC.展开更多
In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit...In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.展开更多
基金The support of Prince Sultan University for paying the Article Processing Charge(APC)of this publication and their support.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300).
文摘Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.
基金Prince Sultan University for their supportPrincess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300)supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1447).
文摘The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy,as the process remains time-intensive and costly.Therefore,machine learning techniques have emerged as powerful alternatives.This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC.For this purpose,500 data points,including 8 input parameters that affect the fracture energy of FRC,are collected fromthree-point bending tests and employed to train and evaluate themachine learning techniques.The findings showed that Gaussian process regression(GPR)outperforms all other models in terms of predictive accuracy,achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation.It is then followed by support vector regression(SVR)and extreme gradient boosting regression(XGBR),whereas K-nearest neighbours(KNN)and random forest regression(RFR)show the weakest predictions.The superiority of GPR is further reinforced in a 5-fold cross-validation,where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance.Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy,cementing its claim.The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction,whereas the glass fiber dominates energy absorption amongst the other types of fibers.In addition,increasing the water-to-cement(W/C)ratio from 0.30 to 0.50 yields a significant improvement in fracture energy,which aligns well with the machine learning predictions.Similarly,loading rate positively correlates with fracture energy,highlighting the strain-rate sensitivity of FRC.This work is the missing link to integrate experimental fracture mechanics and computational intelligence,optimally and reasonably predicting and refining the fracture energy of FRC.
基金supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2024/R/1445)The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Group Research Project (Grant No.RGP.2/357/44).
文摘In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.
