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A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

Identification of the noise generating mechanisms of gravity action and vibrator stimulated sliding chutes has resulted in the development of practical and effective noise abatement treatments for both. In the case of gravity action chutes the application of foam-backed thin and narrow spring steel plates on the chute surface achieves the desired effect with noise reduction of 14 to 25 dB(A). With vibrator stimulated chutes progressive steps were taken to attenuate source noise, chute radiation noise and the non-productive component of the force vector from the vibrator, resulting in noise reduction of 25 to 30 dB(A).