Durable aggregates are essential for the stability and longevity of construction projects,and the Los Angeles Abrasion(LAA)value is a widely used indicator of aggregate durability.However,direct LAA testing is time-co...Durable aggregates are essential for the stability and longevity of construction projects,and the Los Angeles Abrasion(LAA)value is a widely used indicator of aggregate durability.However,direct LAA testing is time-consuming,costly,and requires specialized facilities.This study introduces a novel and efficient alternative by developing two hybrid machine learning models Artificial Neural Network integrated with Particle Swarm Optimization(ANN-PSO)and Artificial Neural Network combined with Teaching-Learning-Based Optimization(ANN-TLBO)for the first time to predict LAA values from petrographic characteristics of carbonate rocks.A total of 160 rock samples from 10 geological formations in Pakistan’s Salt Range were analyzed through petrographic examination and LAA testing.The predictive performance of the proposed models was compared with established techniques,including Multiple Linear Regression,Random Forest,Adaptive Boosting,Gradient Boosting,K-Nearest Neighbors,and Multilayer Perceptron.Results demonstrate that ANN-PSO significantly outperformed all other approaches,achieving an R^(2)of 0.9982,RMSE of 0.209,MAE of 0.159,and MAPE of 0.009.Model robustness was validated using Taylor plots,REC curves,and an external dataset.Sensitivity analysis identified quartz,calcite,and feldspar as the most influential factors affecting LAA prediction.The findings confirm that the ANN-PSO and ANN-TLBO models provide highly accurate,cost-effective,and practical alternatives to traditional LAA testing.This innovative approach advances rock mechanics and material assessment,offering engineers and geologists enhanced tools for aggregate selection and durability evaluation in infrastructure projects.展开更多
This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm...This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.展开更多
In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath f...In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.展开更多
文摘Durable aggregates are essential for the stability and longevity of construction projects,and the Los Angeles Abrasion(LAA)value is a widely used indicator of aggregate durability.However,direct LAA testing is time-consuming,costly,and requires specialized facilities.This study introduces a novel and efficient alternative by developing two hybrid machine learning models Artificial Neural Network integrated with Particle Swarm Optimization(ANN-PSO)and Artificial Neural Network combined with Teaching-Learning-Based Optimization(ANN-TLBO)for the first time to predict LAA values from petrographic characteristics of carbonate rocks.A total of 160 rock samples from 10 geological formations in Pakistan’s Salt Range were analyzed through petrographic examination and LAA testing.The predictive performance of the proposed models was compared with established techniques,including Multiple Linear Regression,Random Forest,Adaptive Boosting,Gradient Boosting,K-Nearest Neighbors,and Multilayer Perceptron.Results demonstrate that ANN-PSO significantly outperformed all other approaches,achieving an R^(2)of 0.9982,RMSE of 0.209,MAE of 0.159,and MAPE of 0.009.Model robustness was validated using Taylor plots,REC curves,and an external dataset.Sensitivity analysis identified quartz,calcite,and feldspar as the most influential factors affecting LAA prediction.The findings confirm that the ANN-PSO and ANN-TLBO models provide highly accurate,cost-effective,and practical alternatives to traditional LAA testing.This innovative approach advances rock mechanics and material assessment,offering engineers and geologists enhanced tools for aggregate selection and durability evaluation in infrastructure projects.
基金financially supported by High-end Foreign Expert program(G20190022002)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZDK201900102)Chongqing Construction Science and Technology Plan Project(2019-0045),that are gratefully acknowledged。
文摘This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.
基金Project(50175034) supported by the National Natural Science Foundation of China
文摘In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.
