The database of 254 rockburst events was examined for rockburst damage classification using stochastic gradient boosting （SGB） methods. Five potentially relevant indicators including the stress condition factor, the...The database of 254 rockburst events was examined for rockburst damage classification using stochastic gradient boosting （SGB） methods. Five potentially relevant indicators including the stress condition factor, the ground support system capacity, the excavation span, the geological structure and the peak particle velocity of rockburst sites were analyzed. The performance of the model was evaluated using a 10 folds cross-validation （CV） procedure with 80%of original data during modeling, and an external testing set （20%） was employed to validate the prediction performance of the SGB model. Two accuracy measures for multi-class problems were employed： classification accuracy rate and Cohen’s Kappa. The accuracy analysis together with Kappa for the rockburst damage dataset reveals that the SGB model for the prediction of rockburst damage is acceptable.展开更多
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
This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to...This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to predict the compressive strength(CS)and tensile strength(TS)of engineered cementitious composites(ECC).XGBoost emerged as the superior model among the four ML models,providing an interpretable and highly accurate predictive framework.To optimize the model performance,hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80%training and 20%testing subsets.The Shapley Additive Explanations(SHAP)algorithm was also employed to reveal the impact of important features,such as the water/binder ratio,fly ash content,and water reducer dosage,on the model’s predictions and their interrelationships.The XGBoost demonstrates the most exemplary performance,as reflected in the R^(2)values of 0.92 and 0.97 for CS and TS testing,respectively.The SHAP analysis provided insights into the impact of individual features on CS and TS,shedding light on how specific characteristics influence the predictive accuracy of these properties.This highly accurate prediction model uncovers insights into correlated features,aids in creating new mix designs of ECC,and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.展开更多
基金Project(2015CX005)supported by the Innovation Driven Plan of Central South University of ChinaProject supported by the Sheng Hua Lie Ying Program of Central South University,China
文摘The database of 254 rockburst events was examined for rockburst damage classification using stochastic gradient boosting （SGB） methods. Five potentially relevant indicators including the stress condition factor, the ground support system capacity, the excavation span, the geological structure and the peak particle velocity of rockburst sites were analyzed. The performance of the model was evaluated using a 10 folds cross-validation （CV） procedure with 80%of original data during modeling, and an external testing set （20%） was employed to validate the prediction performance of the SGB model. Two accuracy measures for multi-class problems were employed： classification accuracy rate and Cohen’s Kappa. The accuracy analysis together with Kappa for the rockburst damage dataset reveals that the SGB model for the prediction of rockburst damage is acceptable.
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.
文摘This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to predict the compressive strength(CS)and tensile strength(TS)of engineered cementitious composites(ECC).XGBoost emerged as the superior model among the four ML models,providing an interpretable and highly accurate predictive framework.To optimize the model performance,hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80%training and 20%testing subsets.The Shapley Additive Explanations(SHAP)algorithm was also employed to reveal the impact of important features,such as the water/binder ratio,fly ash content,and water reducer dosage,on the model’s predictions and their interrelationships.The XGBoost demonstrates the most exemplary performance,as reflected in the R^(2)values of 0.92 and 0.97 for CS and TS testing,respectively.The SHAP analysis provided insights into the impact of individual features on CS and TS,shedding light on how specific characteristics influence the predictive accuracy of these properties.This highly accurate prediction model uncovers insights into correlated features,aids in creating new mix designs of ECC,and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.
