Machine learning approaches for stability prediction of rectangular tunnels in natural clays based on MLP and RBF neural networks

Jitchaijaroen, Wittaya; Keawsawasvong, Suraparb; Wipulanusat, Warit; Kumar, Divesh Ranjan; Jamsawang, Pitthaya; Sunkpho, Jirapon

doi:10.1016/j.iswa.2024.200329

Cited by 14 publications

(2 citation statements)

References 43 publications

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“…Multilayer Perceptron (MLP), also known as Artificial Neural Network (ANN), is built upon a single-layer neural network by introducing one or more hidden layers, creating a neural network with multiple layers, hence the name "multilayer perceptron". The hidden layers are located between the input layer and the output layer [20,21]. The simplest MLP contains only one hidden layer, making it a three-layer structure, as shown in Figure 4.…”

Section: Algorithm Selectionmentioning

confidence: 99%

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

Zhang,

Ma,

Ren

et al. 2024

Mathematics

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With the continuous deepening of educational reform, a large number of educational policies, programs, and research reports have emerged, bringing a heavy burden of information processing and management to educators. Traditional manual classification and archiving methods are inefficient and susceptible to subjective factors. Therefore, an automated method is needed to quickly and accurately classify and archive documents into their respective categories. Based on this, this paper proposes a design of an automatic document classification system for educational reform based on the Naive Bayes algorithm to address the challenges of document management in the education field. Firstly, the relevant literature and document data in the field of educational reform are collected and organized to establish an annotated dataset for model detection. Secondly, the raw data are preprocessed by cleaning and transforming the original text data to make them more suitable for input into machine learning algorithms. Thirdly, various algorithms are trained and selected to determine the best algorithm for classifying educational reform documents. Finally, based on the determined algorithm, a corresponding classification software is designed to automatically classify and archive educational reform documents for analysis. Through experimental evaluation and result analysis, this research demonstrates the effectiveness and accuracy of the education reform document automatic classification system based on the Naive Bayes algorithm. This method can efficiently classify a large number of documents into their respective categories quickly and accurately, thereby improving the efficiency of educators and their information management capabilities. In the future, further exploration of feature extraction methods and machine learning algorithms can be conducted to optimize the classification performance and apply this method to practical management and decision-making in the education field.

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Section: Algorithm Selectionmentioning

confidence: 99%

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

Zhang,

Ma,

Ren

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…In addition, there are various models and algorithms in the field of corrosion prediction [22]. These include artificial neural network models (such as FF, MLP, and RBF) [23][24][25] and optimization algorithms (such as WOA, Bat, Firefly, and GWO) [26][27][28]. Compared to other optimization algorithms (e.g., WOA, Bat, and Firefly), PSO has faster convergence and global search capability [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Ren,

Chen,

Yang

et al. 2024

Applied Sciences

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External corrosion poses a significant threat to the integrity and lifespan of buried pipelines. Accurate prediction of corrosion rates is important for the safe and efficient transportation of oil and natural gas. However, limited data availability often impacts the performance of conventional predictive models. This study proposes a novel composite modeling approach integrating kernel principal component analysis (KPCA), particle swarm optimization (PSO), and extreme learning machine (ELM). The key innovation lies in using KPCA for reducing the dimensionality of complex input data combined with PSO for optimizing the parameters of the ELM network. The model was rigorously trained on 12 different datasets and comprehensively evaluated using metrics such as the coefficient of determination (R2), standard deviation (SD), mean relative error (MRE), and root mean square error (RMSE). The results show that KPCA effectively extracted four primary components, accounting for 91.33% of the data variability. The KPCA-PSO-ELM composite model outperformed independent models with a higher accuracy, achieving an R2 of 99.59% and an RMSE of only 0.0029%. The model comprehensively considered various indicators under the conditions of limited data. The model significantly improved the prediction accuracy and provides a guarantee for the safety of oil and gas transport.

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A novel approach to estimate rock deformation under uniaxial compression using a machine learning technique

T.,

kumar,

Kumar

et al. 2024

Bull Eng Geol Environ

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Machine learning approaches for stability prediction of rectangular tunnels in natural clays based on MLP and RBF neural networks

Cited by 14 publications

References 43 publications

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

A novel approach to estimate rock deformation under uniaxial compression using a machine learning technique

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