Robust design of self-starting drains using Random Forest

Ge, Qi; Liu, Zhongqiang; Sun, Hongyue; Lang, Dominik H.; Shuai, Fei-xiang; Shang, Yongjia; Zhang, Yingqiu

doi:10.1007/s11629-020-6202-4

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…RF is a reliable and powerful machine learning algorithm, proposed by Leo Breiman and Adele Cutler [ 23 ]. The RF is a classification and regression algorithm that belongs to the bagging (i.e., bootstrap aggregation) algorithm in integrated learning [ 24 ]. RFs are characterized by decision trees (DTs), in which a model is constructed based on a randomized training set; the values of the different DTs are not correlated and are calculated independently, and the average of the results obtained using these decision trees is used in the prediction process [ 25 , 26 , 27 , 28 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer

Huang

et al. 2022

Materials

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Blast furnace slag (BFS) and fly ash (FA), as mining-associated solid wastes with good pozzolanic effects, can be combined with superplasticizer to prepare concrete with less cement utilization. Considering the important influence of strength on concrete design, random forest (RF) and particle swarm optimization (PSO) methods were combined to construct a prediction model and carry out hyper-parameter tuning in this study. Principal component analysis (PCA) was used to reduce the dimension of input features. The correlation coefficient (R), the explanatory variance score (EVS), the mean absolute error (MAE) and the mean square error (MSE) were used to evaluate the performance of the model. R = 0.954, EVS = 0.901, MAE = 3.746, and MSE = 27.535 of the optimal RF-PSO model on the testing set indicated the high generalization ability. After PCA dimensionality reduction, the R value decreased from 0.954 to 0.88, which was not necessary for the current dataset. Sensitivity analysis showed that cement was the most important feature, followed by water, superplasticizer, fine aggregate, BFS, coarse aggregate and FA, which was beneficial to the design of concrete schemes in practical projects. The method proposed in this study for estimation of the compressive strength of BFS-FA-superplasticizer concrete fills the research gap and has potential engineering application value.

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Section: Methodsmentioning

confidence: 99%

Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer

Huang

et al. 2022

Materials

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“…Landslides could have disastrous consequences for the life and economy in mountainous regions worldwide (Lü et al, 2021; Sun et al, 2021; Yu et al, 2021; Zheng et al, 2021). Slope surface displacement is one of the most intuitive demonstrations of the development and stability of the landslide (Cheng et al, 2021; Ge, Liu, et al, 2021; Wei et al, 2020). Dependable prediction of displacement is essential for early warning of landslides and contributes to protecting the population and reducing economic losses (Li, Long, et al, 2021; Zhang, Zhang, Liao, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A data‐driven intelligent model for landslide displacement prediction

Sun

Liu

et al. 2023

Geological Journal

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Landslides with step‐like deformation features are widely distributed in the Three Gorges Reservoir area (TGR) of China, posing a severe hazard to the inhabitants of this region. This paper proposes a multi‐input and multi‐output intelligent integrated displacement prediction model for landslides with step‐like displacement patterns. In this new model, three interconnected and information‐transmitted functional sub‐models are integrated. Unsupervised learning is used to identify different landslide deformation states automatically, and the imbalance classification and explainable artificial intelligence techniques are introduced for qualitative prediction and information filtering. Probability theory and deep machine learning are adopted to provide deterministically predicted values and quantify their uncertainty. The case study of the Baijiabao landslide in the TGR region proves that the proposed model performs satisfactorily in both point and interval predictions. The intelligent integrated model can also provide the forecast of landslide deformation states, visual input information filtering and back analysis of influencing factors, which are valuable to landslide early warning and risk management.

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A new anchor-siphon drainage combined method used for slope stabilization

Sun,

Wang,

et al. 2023

J. Mt. Sci.

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Robust design of self-starting drains using Random Forest

Cited by 5 publications

References 48 publications

Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer

Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer

A data‐driven intelligent model for landslide displacement prediction

A new anchor-siphon drainage combined method used for slope stabilization

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