Flowchart of proposed framework (IMAGE)
Caption
Based on the initial dataset, X, the corresponding feature values, Y, are measured by NDT inspection (via human or autonomous robots). Then, the Bayesian Optimization (BO) loop is performed for GP-assisted active learning to find the most promising location for NDT inspection. Based on the given dataset, D = [X, Y], all candidate kernels (M) are employed to construct the GP. Based on the BIC (Bayesian Information Criterion), the best model (GP(x; M, D)) is automatically selected (i.e., automated model selection). The best model is employed with the acquisition function (αEI-GF) for active learning. By implementing the BO for maximizing αEI-GF, the most promising location is selected and evaluated by NDT inspection to obtain new data (xnew and ynew). Then, dataset D is augmented by adding new data (D ∪ {(xnew, ynew)}). The above mentioned procedure is repeated until the maximum number of samples is reached.
Credit
Korea Institute of Civil Engineering and Building Technology
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