An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Li, Yanyan; Chen, Jianping; Shang, Yanjun

doi:10.3390/su9010032

Cited by 6 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors suggested the RVM equation developed might be utilized as an effective tool for predicting the status of epimetamorphic rock slopes. Li et al (2017) developed a novel model based on RVM for estimating the failure probability of slopes, with a focus to the characteristics of failed slopes along the lower reaches of the Jinsha River, close to the Wudongde dam site. The authors investigated seven criteria that influence the occurrence of landslides as environmental factors: lithology, b , H , slope aspect, slope structure, distance from faults, and land use.…”

Section: Application Of Gprmentioning

confidence: 99%

“…where s is the kernel parameter. This function is not affected by outliers, and it can handle nonlinear relationships between class labels and characteristics (Li et al, 2017). SVM/SVR is one of the most extensively used regression-based ML methods for detecting decision boundaries and separating various groups (Li and Dong, 2012;Samui, 2013).…”

Section: Brief Overview Of Different Ai Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Intelligence Techniques in Slope Stability Analysis

Bardhan

Samui

2022

International Journal of Geotechnical Earthquake Engineering

View full text Add to dashboard Cite

Artificial intelligence (AI) techniques have become a trusted methodology among researchers in the recent decade for handling a variety of geotechnical and geological problems. Machine learning (ML) algorithms are distinguished by their superior feature learning and expression capabilities as compared to traditional approaches, attracting researchers from a variety of domains to their growing number of applications. Different ML models are extensively used in the field of geotechnical engineering to accounting for the inherent spatial variability of soils in slope stability assessments. This study presents a brief overview of the application of several AI techniques in the area of slope stability, including adaptive neuro-fuzzy inference system, artificial neural network, extreme learning machine, functional network, genetic programming, Gaussian process regression, least-square support vector machine, multivariate adaptive regression spline, minimax probability machine regression, relevance vector machine, and support vector machine.

show abstract

Section: Application Of Gprmentioning

confidence: 99%

Section: Brief Overview Of Different Ai Techniquesmentioning

confidence: 99%

Application of Artificial Intelligence Techniques in Slope Stability Analysis

Bardhan

Samui

2022

International Journal of Geotechnical Earthquake Engineering

View full text Add to dashboard Cite

show abstract

“…(b) It is common that there are correlations between product attributes and between requirements; e.g., the shell material is correlated to the weight of smartphones. This reality against the pre-assumption of mutually independent between attributes for some intelligent classifiers, e.g., NB (Li et al 2016;Wang and Tseng 2015). (c) Without the Bayesian framework, intelligent classifiers only provide the value of class, i.e., the class in which an individual belongs (Read et al 2011).…”

Section: Feasible Configurationsmentioning

confidence: 99%

A product configuration approach based on online data

Jiao

Yang

2018

J Intell Manuf

View full text Add to dashboard Cite

Product design is greatly influenced by product configuration processes and can be suspended or result in failure if the configuration process consumes too much time, cost, or resources; such results can also occur if the end products manufactured based on configurations failed to satisfy customers. Therefore, a configuration approach that saves time, cost, and resources, as well as highly satisfies customers, is necessary and significant. Against the background, this study proposes a configuration approach that uses online data to map customer requirements into product configurations, including the product transaction data and customer review data. The approach generates feasible configurations initially by using transaction data. Next, the approach produces training samples based on positive customer review data. Lastly, the intelligent classifier is trained by the training samples and is utilized to select final configurations from feasible configurations to satisfy customer requirements. A real-world design case of smartphones is used to illustrate the proposed approach, and the results indicate that this approach saves time, cost, and resources and is competitive compared with other product configuration methods. This novel configuration approach provides designers and companies with a superior and efficient method to complete configuration tasks with competiveness and low risk and adds value to the usability and analysis of online data.

show abstract

“…Relevant vector machine (RVM) is a probability model based on sparse Bayesian learning theory. Under a conditional distribution and the associated maximum likelihood estimation, the nonlinear problems in low-dimensional space are transformed into linear problems in high-dimensional space by kernel functions [17][18][19][20]. RVM has the advantages of good learning ability and strong generalization ability, provided that a suitable kernel function is selected and the hyper-parameters are set correctly.…”

Section: Introductionmentioning

confidence: 99%

“…RVM has the advantages of good learning ability and strong generalization ability, provided that a suitable kernel function is selected and the hyper-parameters are set correctly. The method of RVM has been widely used in academia to deal with geotechnical problems, such as slope stability and reliability analysis [17][18][19][20][21][22], the ultimate capacity of driven piles [23], and seismic liquefaction predictions [23,24]. In previous studies, the method of RVM is combined with global optimization algorithms to build the optimal models in a wide study from predicting geotechnical parameters, self-compacting concrete parameters [25][26][27][28][29], to estimating oil price and river water levels [30,31].…”

Section: Introductionmentioning

confidence: 99%

Predictions of Geological Interface Using Relevant Vector Machine with Borehole Data

Ji¹,

Lu²,

Guo³

et al. 2022

Sustainability

View full text Add to dashboard Cite

Due to the discreteness, sparsity, multidimensionality, and incompleteness of geotechnical investigation data, traditional methods cannot reasonably predict complex stratigraphic profiles, thus hindering the three-dimensional (3D) reconstruction of geological formation that is vital to the visualization and digitization of geotechnical engineering. The machine learning method of relevant vector machine (RVM) is employed in this work to predict the 3D stratigraphic profile based on limited geotechnical borehole data. The hyper-parameters of kernel functions are determined by maximizing the marginal likelihood using the particle swarm optimization algorithm. Three kinds of kernel functions are employed to investigate the prediction performance of the proposed method in both 2D analysis and 3D analysis. The 2D analysis shows that the Gauss kernel function is more suitable to deal with nonlinear problems but is more sensitive to the number of training data and it is better to use spline kernel functions for RVM model trainings when there are few geotechnical investigation data. In the 3D analysis, it is found that the prediction result of the spline kernel function is the best and the relevant vector machine model with a spline kernel function performs better in the area with a fast change in geological formation. In general, the RVM model can be used to achieve the purpose of 3D stratigraphic reconstruction.

show abstract

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Cited by 6 publications

References 41 publications

Application of Artificial Intelligence Techniques in Slope Stability Analysis

Application of Artificial Intelligence Techniques in Slope Stability Analysis

A product configuration approach based on online data

Predictions of Geological Interface Using Relevant Vector Machine with Borehole Data

Contact Info

Product

Resources

About