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| 1 | Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment显示文摘This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects. | Abidhan Bardhan Navid Kardani Anasua GuhaRay Avijit Burman Pijush Samui Yanmei Zhang | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 2 |
| 2 | Predictability performance enhancement for suspended sediment in rivers:Inspection of newly developed hybrid adaptive neuro-fuzzy system model显示文摘Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams,the durability of hydroelectric-equipment,river susceptibility to pollution,suitability for navigation,and potential for aesthetics and fish habitat.The capability of a new machine learning model,fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm(ANFIS-FCM-PSOGSA)in improving the estimation accuracy of river suspended sediment loads(SSLs)is investigated in the current study.The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization(ANFIS-FCM-PSO),ANFIS-FCM,and sediment rating curve(SRC)models.Various input combinations involving lagged river flow(Q)and suspended sediment(S)values were used for model development.The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs.The model performance was assessed using the root mean square error(RMSE),mean absolute error(MAE),Nash eSutcliffe Efficiency(NSE),and coefficient of determination(R^(2))and several graphical comparison methods.The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO(or ANFIS-FCM)models by 8.14%(1.72%),14.7%(5.71%),12.5%(2.27%),and 25.6%(1.86%),in terms of the RMSE,MAE,NSE and R^(2),respectively.The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load.The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification. | Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi | 2022 | International Journal of Sediment Research2022，37，3： | 0 |
| 3 | Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning显示文摘With the development of landslide monitoring system,many attempts have been made to predict landslide failure-time utilizing monitoring data of displacements.Classical models(e.g.,Verhulst,GM(1,1),and Saito models)that consider the characteristics of landslide displacement to determine the failuretime have been investigated extensively.In practice,monitoring is continuously implemented with monitoring data-set updated,meaning that the predicted landslide life expectancy(i.e.,the lag between the predicted failure-time and time node at each instant of conducting the prediction)should be re-evaluated with time.This manner is termed“dynamic prediction”.However,the performances of the classical models have not been discussed in the context of the dynamic prediction yet.In this study,such performances are investigated firstly,and disadvantages of the classical models are then reported,incorporating the monitoring data from four real landslides.Subsequently,a more qualified ensemble model is proposed,where the individual classical models are integrated by machine learning(ML)-based meta-model.To evaluate the quality of the models under the dynamic prediction,a novel indicator termed“discredit index(b)”is proposed,and a higher value of b indicates lower prediction quality.It is found that Verhulst and Saito models would produce predicted results with significantly higher b,while GM(1,1)model would indicate results with the highest mean absolute error.Meanwhile,the ensemble models are found to be more accurate and qualified than the classical models.Here,the performance of decision tree regression-based ensemble model is the best among the various ML-based ensemble models. | Lei-Lei Liu Hao-Dong Yin Ting Xiao Lei Huang Yung-Ming Cheng | 2024 | Geoscience Frontiers2024，15，2： | 0 |
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