The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation networks.This study introduces a comprehensive methodology...The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation networks.This study introduces a comprehensive methodology for soil slope stability evaluation,employing Monte Carlo Simulation(MCS)and Subset Simulation(SS)with the"UPSS 3.0 Add-in"in MS-Excel.Focused on an 11.693-meter embankment with a soil slope(inclination ratio of 2H:1V),the investigation considers earthquake coefficients(kh)and pore water pressure ratios(ru)following Indian zoning requirements.The chance of slope failure showed a considerable increase as the Coefficient of Variation(COV),seismic coefficients(kh),and pore water pressure ratios(ru)experienced an escalation.The SS approach showed exceptional efficacy in calculating odds of failure that are notably low.Within computational modeling,the study optimized the worst-case scenario using ANFIS-GA,ANFIS-GWO,ANFIS-PSO,and ANFIS-BBO models.The ANFIS-PSO model exhibits exceptional accuracy(training R2=0.9011,RMSE=0.0549;testing R2=0.8968,RMSE=0.0615),emerging as the most promising.This study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation infrastructure.These findings contribute to the enhancement of safety and reliability in real-world situations.展开更多
Predicting the punching shear strength(PSS)of flat slabs is crucial for ensuring the safety and efficiency of reinforced concrete structures.This study presents novel hybrid approaches combining support vector regress...Predicting the punching shear strength(PSS)of flat slabs is crucial for ensuring the safety and efficiency of reinforced concrete structures.This study presents novel hybrid approaches combining support vector regression(SVR)with advanced optimization algorithms to enhance the accuracy of PSS predictions.Four optimization algorithms,krill herd algorithm,biogeography-based optimization,equilibrium optimizer,and genetic algorithm(GA),were employed to optimize SVR parameters for improved PSS estimation.A data set of 264 samples with seven design parameters was used as input to model PSS.Sensitivity analysis and comparison to standard equations were conducted to evaluate the significance of input variables and the reliability of proposed models in predicting PSS.The results demonstrated that integrating optimization algorithms significantly improved the predictive performance of SVR models.Among the proposed approaches,the SVR-GA model achieved the highest accuracy,with a correlation coefficient of 0.95 and a mean absolute error of 132.28 kN in the testing phase.Sensitivity analysis revealed that slab thickness and depth,followed by concrete strength,were the most influential parameters for predicting PSS.The proposed SVR-GA model was found more accurate than American,European,and Canadian concrete code standards in modeling PSS.These findings underscore the effectiveness of hybrid SVR models in accurately modeling PSS and highlight the importance of optimizing input features to ensure robust predictions.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
文摘The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation networks.This study introduces a comprehensive methodology for soil slope stability evaluation,employing Monte Carlo Simulation(MCS)and Subset Simulation(SS)with the"UPSS 3.0 Add-in"in MS-Excel.Focused on an 11.693-meter embankment with a soil slope(inclination ratio of 2H:1V),the investigation considers earthquake coefficients(kh)and pore water pressure ratios(ru)following Indian zoning requirements.The chance of slope failure showed a considerable increase as the Coefficient of Variation(COV),seismic coefficients(kh),and pore water pressure ratios(ru)experienced an escalation.The SS approach showed exceptional efficacy in calculating odds of failure that are notably low.Within computational modeling,the study optimized the worst-case scenario using ANFIS-GA,ANFIS-GWO,ANFIS-PSO,and ANFIS-BBO models.The ANFIS-PSO model exhibits exceptional accuracy(training R2=0.9011,RMSE=0.0549;testing R2=0.8968,RMSE=0.0615),emerging as the most promising.This study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation infrastructure.These findings contribute to the enhancement of safety and reliability in real-world situations.
基金supported by the Ministry of Trade,Industry and Energy and the Institute for Industrial Technology Evaluation and Management(KEIT)in 2022(No.RS-2022-00154935,Title:Manufacturing of non-carbonate raw materials and development of cement technology to replace limestone with 5 wt.%or more)。
文摘Predicting the punching shear strength(PSS)of flat slabs is crucial for ensuring the safety and efficiency of reinforced concrete structures.This study presents novel hybrid approaches combining support vector regression(SVR)with advanced optimization algorithms to enhance the accuracy of PSS predictions.Four optimization algorithms,krill herd algorithm,biogeography-based optimization,equilibrium optimizer,and genetic algorithm(GA),were employed to optimize SVR parameters for improved PSS estimation.A data set of 264 samples with seven design parameters was used as input to model PSS.Sensitivity analysis and comparison to standard equations were conducted to evaluate the significance of input variables and the reliability of proposed models in predicting PSS.The results demonstrated that integrating optimization algorithms significantly improved the predictive performance of SVR models.Among the proposed approaches,the SVR-GA model achieved the highest accuracy,with a correlation coefficient of 0.95 and a mean absolute error of 132.28 kN in the testing phase.Sensitivity analysis revealed that slab thickness and depth,followed by concrete strength,were the most influential parameters for predicting PSS.The proposed SVR-GA model was found more accurate than American,European,and Canadian concrete code standards in modeling PSS.These findings underscore the effectiveness of hybrid SVR models in accurately modeling PSS and highlight the importance of optimizing input features to ensure robust predictions.
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.
