An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis

Algorithm 1

Pseudocode of hyperparameter tuning for SBL model.

	Inputs: The training dataset and search range of hyper-parameters .
	Outputs: The optimal hyperparameter
	Initialize: Population size , sub-population size .
	() Compute the target function value using equation (14).
	Identify the best and worst solutions, respectively.
	Update the solutions by equation (13), and compute the new target function value .
	Accept the new solution, .
	Reject the new solution.
	The termination condition is achieved
	Output the search results in th sub-population.
	Return to the key step ().
	Find the best solutions among sub-populations.
	Find the best solutions from populations, as well as .