Gradient Descent (GD) and Conjugate Gradient (CG) methods are among the most effective iterative algorithms for solving unconstrained optimization problems, particularly in machine learning and statistical modeling, w...
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During the construction of shield tunnels, the tunnel lining often has the problem of local or overall upward movement in soft soil areas. Excessive upward movement will lead to lining dislocation, cracks, damage, and...
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During the construction of shield tunnels, the tunnel lining often has the problem of local or overall upward movement in soft soil areas. Excessive upward movement will lead to lining dislocation, cracks, damage, and even axis deviation. This paper elaborates on how to predict the process of tunnel lining upward movement using machine learning algorithms and field monitoring data systematically. First, fourteen input variables including shield operational parameters, tunnel geometry, geological conditions and anomalous condition are considered to predict the upward displacement of twelve output variables that represent the process of the upward move-ment of the tunnel lining. In addition, 80% field monitoring data (81 datasets) are selected randomly as the training set, and the remaining 20% (20 datasets) are the test set. Then, the average of 5-fold cross validation mean absolute error is regarded as the fitness function of optimization algorithms to find the optimal hyper-parameters. Finally, the prediction performance of four machine learning (ML) algorithms back-propagation neural network (BPNN), general regression neural network (GRNN), extreme learning machine (ELM), and support vector machine (SVM) optimized by particle swarm optimization (PSO) and genetic algorithm (GA) were compared. All ML algorithms except BPNN predicted successfully the trend of upward movement of tunnel lining. In particular, PSO-GRNN accurately captures the evolution of upward displacement in different periods of each ring with the lowest errors and the largest correlation coefficient values.
This is a set of lecture notes for a Ph.D.-level course on quantumalgorithms, with an emphasis on quantum optimization algorithms. It isdeveloped for applied mathematicians and engineers, and requires no previousbackg...
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Receiver Operating Characteristic (ROC) curves are useful for evaluation in binary classification and changepoint detection, but difficult to use for learning since the Area Under the Curve (AUC) is piecewise constant...
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A suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence (AI). These almost all result from training flexible algorithms to solve difficult optimization problems spec...
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A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary al...
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In this article, we introduce an original hybrid quantum-classical algorithm based on a variational quantum algorithm for solving systems of differential equations. The algorithm relies on a spectral method, which inv...
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Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider stochastic formulations of optimizatio...
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Equation discovery methods hold promise for extracting knowledge from physics-related data. However, existing approaches often require substantial prior information that significantly reduces the amount of knowledge e...
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Tuning effective step sizes is crucial for the stability and efficiency of optimization algorithms. While adaptive coordinate-wise step sizes tuning methods have been explored in first-order methods, second-order meth...
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