Deep Bayesian neural networks for damage quantification in miter gates of navigation locks

Abstract

Inland navigation infrastructure like locks and dams form a vital part of the global economy. Locks facilitate the transport of hundreds of millions of dollars’ worth of goods on a daily basis. A primary cause for downtime of locks in the United States is damage to lock gates. Current inspection methods involve the complete closure of locks to visually inspect for damage. A common target of such inspections is the identification of “gaps” that form along the bearing surface boundary of miter gates. These gaps accelerate the fatigue failure of the gate by disrupting the designed load distribution mechanism. This article presents a novel engineering application of structural health monitoring for full-scale civil infrastructure with a method to automatically quantify the damage quantity of interest, that is, the gaps using measured strain data. We propose a framework for damage estimation of full-scale civil infrastructure in general and miter gates in particular, leveraging recent advances in deep Bayesian learning. A new two-term loss function is produced to increase the accuracy of the trained networks and the model uncertainties are conveyed using Monte Carlo dropout. In addition, we propose a strategy to model bearing surface gaps using non-linear contact analyses and use the proposed model to determine the sensitivity of measured strains to damage. The proposed framework is implemented for the miter gates at the Greenup locks and dam. Finally, the proposed methodology is validated using measured data. Slopes measured from the lock gate are used as the input to the trained networks to estimate the gap depths. The finite element model is updated using the estimated gap depths. The predicted slopes and strains from the updated model are shown to match the measured strains and slopes well. The results demonstrate the efficacy of the approach for damage detection in full-scale civil infrastructure.