Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model

Guo, Deping; Chen, Hemao; Tang, Lun; Chen, Zhixiong; Samui, Pijush

doi:10.1007/s11440-021-01299-2

Cited by 42 publications

(11 citation statements)

References 58 publications

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“…The multigranularity scanning of deep forest can analyze the spatial-temporal relationships among variables and enhance the model's ability to characterize features. Guo, et al [21] used the deep forest model for the rock-burst prediction problem, and concluded that deep forest showed better performance, faster training speed, and easier application than those of deep neural networks (DNNs), and it can adapt to different training set sizes. Yin, et al [22] proposed a deep forest regression method for short-term load forecasting in power systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Deflection Prediction of Rehabilitation Asphalt Pavements through Deep Forest

Wu¹,

Chen²,

Jiang³

2022

Coatings

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The deep forest is a powerful deep-learning algorithm that has been applied in certain fields. In this study, a deep forest (DF) model was developed to predict the central deflection measured by a falling weight deflectometer (FWD). In total, 11,075 samples containing information related to pavement structure, traffic conditions, and weather conditions were extracted from the LTPP dataset. The performance of the DF model with custom backend settings was compared with that of models random forest (RF), multilayer perceptron (MLP), and DF built on the sklearn backend. All four deep-learning algorithms could identify the complex relationship between central deflection and relevant feature variables with high accuracy and stability. The learning and generalization abilities of DF was stronger than those of MLP and RF. The predictive performance and computation time of DF (custom) were better than those of DF (sklearn), indicating that the custom model was superior to the highly encapsulated model with sklearn as the backend. Feature importance analysis indicated that the drop load of FWD was the key factor influencing deflection. In addition, structural number, annual precipitation, and annual kilo equivalent standard axle load (kESAL) are very important features related with deflection. The feature importance of rehabilitation improvement thickness was less than the drop load, climatic factors, kESAL, structural number, and layer thickness.

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Section: Literature Reviewmentioning

confidence: 99%

Deflection Prediction of Rehabilitation Asphalt Pavements through Deep Forest

Wu¹,

Chen²,

Jiang³

2022

Coatings

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“…To investigate the relative importance of features on the predictive performance of machine learning models, the Shapley additive explanations (SHAP) method is used in this study due to its fast implementation for tree-based models. It uses the Shapley values to quantify the contribution of each feature to the prediction based on the coalitional game theory (Lundberg and Lee, 2017;Guo et al, 2021). Generally, the features with higher positive SHAP values tend to pose a more significant influence on the final prediction.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

Zhang

Wang

et al. 2021

Front. Earth Sci.

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Embankments are widespread throughout the world and their safety under seismic conditions is a primary concern in the geotechnical engineering community since the failure events may lead to disastrous consequences. This study proposes an efficient seismic slope stability analysis approach by introducing advanced gradient boosting algorithms, namely Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). A database consisting of 600 datasets is prepared for model calibration and evaluation, where the factor of safety (FS) is regarded as the output and four influential factors are selected as the inputs. For each dataset, the FS corresponding to the four inputs is evaluated using the commercial geotechnical software of Slide2. As an illustration, the proposed approach is applied to the seismic stability analysis of a hypothetical embankment example subjected to water level changes. For comparison, the predictive performance of CatBoost, LightGBM, and XGBoost is investigated. Moreover, the Shapley additive explanations (SHAP) method is used in this study to explore the relative importance of the four features. Results show that all the three gradient boosting algorithms (i.e., CatBoost, LightGBM, and XGBoost) perform well in the prediction of FS for both the training dataset and testing dataset. Among the four influencing factors, the friction angle φ is the most important feature variable, followed by horizontal seismic coefficient Kh, cohesion c, and saturated permeability ks.

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“…Despite the criticism, MARS has been used as a successful model selection tool as a non-parametric modeling technique in many fields of science. For example; effect of gender on musculoskeletal disorders [ 3 ]; possible impact of climate change on temperature and precipitation parameters in the Eastern Black Sea region [ 4 ]; how the use of traditional media and social media effect the political attitudes and behaviors of citizens [ 5 ]; worldwide cases of rockburst due to severe damage to infrastructures and facilities, and factors triggering these rockburst and the rockburst intensity [ 6 ] and comparison of the effectiveness of Islamic bank in developed and developing countries and the relationship between the efficiency of these banks with gross domestic product (GDP) and Sharia Supervisory Board [ 7 ] are examined using MARS technique. Model selection performances of the MARS method (which is obtained by using the GCV criterion) and the Random Ensemble MARS (REMARS) method (which is obtained by using the random forest algorithm) are compared.…”

Section: Introductionmentioning

confidence: 99%