Titlebook: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering a; Wengang Zhang,Yanmei Zhang,Liang Han

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Machine Learning and Applications,By definition from IBM, ML is a branch of AI and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. ML falls into three primary categories: Supervised learning, Unsupervised learning, as well as the Semi-supervised learning.

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Optimization Algorithms and Applications,When you’re trying to make tough decisions about questions that involve an inordinate number of factors, optimization helps you to capture key components to build a mathematical model of the engineering situation, giving you the confidence to make better decisions more quickly.

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Prediction of Undrained Shear Strength Using XGBoost and RF Based on BO,Soft sensitive clays are generally characterized by low shear strength and high compressibility and widely distributed in a near marine environment. Therefore, geotechnical design involving soft sensitive clays is rather challenging (D’Ignazio et al. D’Ignazio et al. Can Geotech J 53:1628–1645, 2016).

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Prediction for TBM Penetration Rate Using Four Hyperparameter Optimization Methods and RF Model,With the continuous development and progress of technology, the emergence of tunnel boring machine (TBM) provides a safer, more effective and cheaper construction method for tunnel digging.

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What We Have Learnt from the Applications,It is well known that the use of SC and more advanced AI techniques including ML, DL and OA has greatly improved the accuracy and efficiency of prediction for the responses of geotechnical engineering structures.

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https://doi.org/10.1057/9781137297082y logic, Machine learning, Probabilistic reasoning, Evolutionary computation, Perceptron, Genetic algorithms, Differential algorithms, Support vector machines, Metaheuristics, Swarm intelligence, Ant colony optimization, Particle optimization, Bayesian networks, Artificial neural networks, Expert sy

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The Evolving Bilateral Aid Architecture,racy. It consist of multiple layers of interconnected nodes, each building upon the previous layer to refine and optimize the prediction or categorization. This progression of computations through the network is called forward propagation. The input and output layers of a deep neural network are cal

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Steen Fryba Christensen,Li Xingn Geol 55:55–63, 2008; Li et al., Environ Earth Sci 60:677–687, 2010; Du et al.,.Landslides 10:203–218, 2013). In some cases, landslides can cause huge losses in human lives and properties as well as impose significant impacts on the environment (Yin et al.,.Landslides 7:339–349, 2010; Zhang et al.,

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