SummaryNavigation locks are critical infrastructure components, and their closure for maintenance and repair can have significant impacts on the global economy. The current state of inspection and monitoring of lock components is generally to close the lock and perform a visual inspection. Whereas structural health monitoring of navigation locks is gaining acceptance, automation of the structural health monitoring process is lacking. This paper reports on efforts to develop an automated damage detection system for miter gates of navigation locks. The study focuses on using strain gage measurements to identify the redistribution of load throughout lock gates in the presence of damage. To eliminate the environmental variability in the data, a new damage-sensitive feature is introduced, termed here as "slope" and defined as the derivative of the strain with respect to the water levels in the lock chamber. The slopes form a new, stationary time series effectively purged of environmental effects. A principal component analysis, a method of analyzing multivariate, stationary time series, is then used to detect significant changes in the statistics of slopes as an indication of damage. To validate the approach, damage is simulated in a finite element model, and the resulting changes in strain from the finite element model are superimposed on the measured data. The results demonstrate the potential of the proposed approach for detecting damage in navigational lock gates.

Purpose This paper aims to qualify traditional concrete mixtures for large-scale material extrusion in an automated, additive manufacturing process or additive construction. Design/methodology/approach A robust and viable automated additive construction process must be developed that has the capability to construct full-scale, habitable structures using materials that are readily available near the location of the construction site. Accordingly, the applicability of conventional concrete mixtures for large-scale material extrusion in an additive construction process was investigated. A qualitative test was proposed in which concrete mixtures were forced through a modified clay extruder and evaluated on performance and potential to be suitable for nozzle extrusion typical of additive construction, or 3D printing with concrete. The concrete mixtures were further subjected to the standard drop table test for flow, and the results for the two tests were compared. Finally, the concrete mixtures were tested for setting time, compressive strength and flexural strength as final indicators for usefulness in large-scale construction. Findings Conventional concrete mixtures, typically with a high percentage of coarse aggregate, were found to be unsuitable for additive construction application due to clogging in the extruder. However, reducing the amount of coarse aggregate provided concrete mixtures that were promising for additive construction while still using materials that are generally available worldwide. Originality/value Much of the work performed in additive manufacturing processes on a construction scale using concrete focuses on unconventional concrete mixtures using synthetic aggregates or no coarse aggregate at all. This paper shows that a concrete mixture using conventional materials can be suitable for material extrusion in additive construction. The use of conventional materials will reduce costs and allow for additive construction to be used worldwide.

The U.S. Army Corps of Engineers (USACE) operates and maintains 236 lock chambers at 191 lock sites on 41 waterways throughout the contiguous United States. Waterway navigational locks are important parts of the nation's infrastructure. Locks enable the flow of billions of dollars of commerce and support efforts for flood control. Proper maintenance of the locks and early detection of damage is crucial; however, due to shrinking budgets, adequate funding to apply traditional scheduled maintenance and visual inspection is not available. Structural health monitoring (SHM) systems have been considered to assist in establishing more efficient maintenance, repair, and replacement priorities for navigational locks. This work was undertaken to develop and implement a real-time methodology that provides lock operators with a robust, accurate warning system of gap(s) at the gate-to-wall interface. This initial effort, which focused on horizontally framed miter gates and on damage that is assumed to take the form of a gap at the gate/wall interface (quoin), developed a methodology to identify the occurrence of damage in miter gate structures using data from strain and water level gages that is collected continuously from the SHM system deployed by USACE. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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.

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