Bayesian Damage Identification Using Strain Data from Lock Gates
Damage identification plays a significant role in the maintenance of navigation locks, which are part of the United States’ $500B replacement value in inland waterway infrastructure; maritime transport disruption for closed lock gates causes substantial e
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Bayesian Damage Identification Using Strain Data from Lock Gates Yichao Yang, Ramin Madarshahian, and Michael D. Todd
Abstract Damage identification plays a significant role in the maintenance of navigation locks, which are part of the United States’ $500B replacement value in inland waterway infrastructure; maritime transport disruption for closed lock gates causes substantial economic and utility losses. Lock gates are normally instrumented with strain gauges, and one of the critical failure modes is the development of a gap between the supporting wall and gate, initiating from quoin and/or pintle part wear. This gap leads to undesirable load distributions that can induce gate failure by overload. The probability of damage exceedance from different values affects repair strategies. This work uses Bayesian inference to identify that damage. The input features are raw strain data, while the loading is assumed unknown. The inherent uncertainty in measurements and model assumptions result in a posterior distribution of parameters of damage such as gap size and location. The results show that the true parameter of damage, which is used to generate simulated data, could be predicted using the posterior.
7.1 Introduction Navigation locks play an important role in waterway transportation system in the US and all over the world [1, 2]. The unexpected closure of these assets, e.g. due to malfunction of structural components, is very costly because it prevents many shippers from fulfilling their scheduled transport missions [3]. To prevent the unscheduled closure of navigation locks in the United States, US Army Corps of Engineers (USACE) designed a discrete, indexed-based condition rating system for components of a navigation lock. Through this rating system, decision-makers are informed about repair and maintenance scenarios [4, 5]. Structural health monitoring of these assets can reduce the uncertainty about assigning a condition to different components of navigation locks. One of the most common damage scenarios in miter gates is the occurrence of gaps in the quoin block, which is an interface between the gate and the supporting wall [6]. When this gap size increases, the area which connects the gate to the supporting wall decreases, leading to stress redistribution that possibly exceeds design or performance limit states. For example, the cyclic nature of loading on the structure may cause fatigue failures in such high-stress zones [7]. Early prediction of gap development in miter gates informs the planning of repair strategies at the time of scheduled closure and to eliminate unscheduled (and much more costly) closures due to an unexpected failure of components of the structure. The most common loading scenario for miter gates is hydrostatic loading due to the different water levels on both sides of the lock chamber. Hydrostatic loading at upstream and downstream will create an internal moment on vertical girders of a lock [8]. Therefore, distribution of stress and subsequently strains on the structure is a function
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