16 Dec 2024

USACE Interference Management Standard v1.0

Abstract: The Interference Management Standard (IMS) is a comprehensive framework designed to streamline the coordination of design, construction, and operation and maintenance models. The IMS provides clear guidelines, defined goals, and objectives to ensure effective interference management. The process encompasses several stages: authoring and compiling models, clash detection, clash analysis, conflict resolution, report compilation, and deliverables submission. By implementing the IMS, users can expect im-proved efficiency and accuracy in model coordination, leading to enhanced project outcomes.

12 Dec 2024

Restoration Monitoring Metric Framework: Integrating Innovative Remote-Sensing Technologies: Comparisons between Field and Remotely Sensed Vegetation Surveys of Restored Forested and Grassland Sites in Ohio

Abstract: Restoration monitoring is generally perceived as costly and time-consuming, yet the concept of universal restoration monitoring metrics is trending for evaluation of restoration performance across spatial scales, project boundaries, and jurisdictions. Natural Resource Damage Assessment and Restoration (NRDAR) practitioners seek to restore natural resources injured by oil spills or hazardous substance releases into the environment. Therefore, a multiagency team [US Army Engineer Research and Development Center (ERDC), US Department of the Interior (DOI), and US Department of Energy (DOE)] developed and field-tested a multitiered monitoring framework, illustrating a range of field and remote-sensing techniques and methodologies. The restoration monitoring framework and field demonstration offer a unique methodology to acquire and evaluate simultaneously collected, multiscale/multiplatform data. The result of this research provides new insights to (1) assist planning, implementing, and monitoring restoration progress and effectiveness; and (2) apply common monitoring methods, endpoints, and metrics to other types of ecosystem restoration initiatives. Although the aim was to inform monitoring and management of areas that had been injured, these methods could also be used to inform restoration monitoring practices in a broader context, benefiting environmental stewardship missions of all project partners.

11 Dec 2024

Adaptive Hydraulics (AdH) Version 4.7.1 Sediment Transport User’s Manual: A 2D Modeling System Developed by the Coastal and Hydraulics Laboratory

Abstract: Guidelines are presented for using the US Army Corps of Engineers (USACE) Adaptive Hydraulics (AdH) modeling software to model 2D shallow water problems with sediment transport (i.e., AdH linked to the Sediment Transport Library [SEDLIB]). This manual describes the inputs necessary to use the SEDLIB sediment transport library from within AdH, to perform coupled hydrodynamic, sediment, and morphological computations. The SEDLIB sediment transport library is intended to be of general use and, as such, examples are given for basic sediment transport of cohesive, noncohesive, and mixed suspended sediment loads and bedload.

9 Dec 2024

Analysis of Microgrid Performance, Reliability, and Resilience (AMPeRRe) Computational Model Novel Analytical Model to Forecast the Outcomes of Installation Power Grids

Abstract: Federal facilities, industrial areas, academic campuses, and communities are working to incorporate greater renewable energy sources and energy storage in their power infrastructure. While renewable sources of energy can—and do—support several facilities, uncertainty still exists about how reliably these sources of energy can support small and critical power systems with higher reliability standards, such as Army installations, tactical microgrids, remote community grids, and emergency response power systems. Maintaining reliability is already a significant challenge for power grids, and those that have a high proportion of renewable energy face particular challenges due to their intermittent power production. This technical report addresses the uncertainty by presenting a new computational model called Analysis of Microgrid Performance, Reliability, and Resilience (AMPeRRe). The model forecasts the power availability, fuel consumption, specific resilience factors, and excess energy production of proposed grids that include renewable energy sources and energy storage. If proposed grids are forecasted to lose power availability, users can apply this model to find which resources are needed to achieve 100% power availability and optimize resource quantities for ideal performance outcomes. AMPeRRe significantly reduces the uncertainty around renewable energy and energy storage in power grids and informs the critical resource investment decisions needed to yield improved long-term outcomes.

4 Dec 2024

Conway Lake Ecosystem Restoration: Soil Investigations to Support Engineering With Nature and Beneficial Use of Dredged Sediment

Purpose: The purpose of this Technical Note is to describe Conway Lake ecosystem restoration adaptive management investigations to evaluate forest planting and soil response to three depths of fine sediment placed over a sand base.

2 Dec 2024

Establishing a Workflow for Near-Seamless Digital Elevation Model Creation in the Great Lakes for ADCIRC Modeling

Abstract: This report introduces a workflow to create near-seamless, regional digital elevation models (DEMs) for use in coupled Advanced Circulation and Simulating Waves Nearshore modeling. The workflow is based in Esri ArcGIS Pro, leveraging the Mosaic Dataset architecture to organize and mosaic survey data sets into near-seamless DEMs. This workflow includes data collection and preprocessing, creation of source and derived mosaic data sets, manual editing of the data set seamlines, the creation of spatial metadata products, and quality assurance and control measures. These steps were implemented for each Great Lake to provide a high-resolution, near-seamless DEM product for modelers. The workflow may also have utility for other regional-scale investigations.

2 Dec 2024

Comparison of Run-Up Models with Field Data

Abstract: Run-up predictions are inherently uncertain, owing to ambiguities in phase-averaged models and inherent complexities of surf and swash-zone hydrodynamics. As a result, different approaches, ranging from simple algebraic expressions to computationally intensive phase-resolving models, have been used in attempt to capture the most relevant run-up processes. Studies quantifiably comparing these methods in terms of physical accuracy and computational speed are needed as new observation technologies and models become available. The current study tests the capability of the new swash formulation of the Coastal Modeling System (CMS) to predict 1D run-up statistics (R2%) collected during an energetic 3 week period on sandy dune-backed beach in Duck, North Carolina. The accuracy and speed of the debut CMS swash formulation is compared with one algebraic model and three other numerical models. Of the four tested numerical models, the CSHORE model computed the results fastest, and the CMS model results had the greatest accuracy. All four numerical models, including XBeach in surfbeat and nonhydrostatic modes, yielded half the error of the algebraic model tested. These findings present an encouraging advancement for phase-averaged coastal models, a critical step towards rapid prediction for near-time deterministic or long-term stochastic guidance.

2 Dec 2024

Adaptive Hydraulics 2D Shallow Water (AdH-SW2D) User’s Manual (Version 4.7.1): Guidelines for Solving 2D Shallow Water Problems with the Adaptive Hydraulics Modeling System

Abstract: Guidelines are presented for using the US Army Corps of Engineers Adaptive Hydraulics modeling software to model 2D shallow water problems. Constituent (nonsediment) transport is also included in this document. Sediment transport instructions are contained in a supplemental user’s guide.

19 Nov 2024

Deep Learning Approach for Accurate Segmentation of Sand Boils in Levee Systems

Abstract: Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNet50V2 architecture, our algorithm effectively leverages learned features for precise detection. We hypothesize that controlled feature extraction using a deeper pretrained CNN model can selectively generate the most relevant feature maps adapting to the domain, thereby improving performance. Experimental results demonstrate that SandBoilNet outperforms state-of-the-art semantic segmentation methods in accurately detecting sand boils, achieving a Balanced Accuracy (BA) of 85.52%, Macro F1-score (MaF1) of 73.12%, and an Intersection over Union (IoU) of 57.43% specifically for sand boils. This proposed approach represents a novel and effective solution for accurately detecting and segmenting sand boils from levee images toward automating the monitoring and maintenance of levee infrastructure.

19 Nov 2024

Widened Attention-Enhanced Atrous Convolutional Network for Efficient Embedded Vision Applications under Resource Constraints

Abstract: Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened attention-enhanced atrous convolution-based efficient network (WACEfNet), a new convolutional neural network designed specifically for real-time visual classification challenges using resource-constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width-wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive over-head. Extensive benchmarking confirms state-of-the-art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high-fidelity real-time analytics across a variety of embedded perception paradigms.