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The ERDC Library supports the mission-related research needs of ERDC scientists and engineers at three physical locations with a centralized library catalog and web site. It also hosts an online digital repository of ERDC-authored reports.

The ERDC Library collection is available for interlibrary loan. Please contact your local library for all interlibrary loan requests. Other requests should be directed to the reference staff.

Additionally the library provides access to:

  • 300,000+ items in the collection - 28,000+ online journals - 34,000+ online books & reports
  • Online research resources including IEEE, Science Direct, Web of Science, RefWorks
  • Collection development and interlibrary loan services
  • Research consultations, training, and outreach services
  • Support for copyright questions and support for research and administrative initiatives

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Publication Notices

  • Modeling the Effect of Increased Sediment Loading on Bed Elevations of the Lower Missouri River

    Purpose: This US Army Corps of Engineers (USACE) National Regional Sediment Management Technical Note (RSM-TN) documents the effects of increased sediment loading to the Missouri River on bed elevations in the lower 498 miles. This was accomplished using a one-dimensional (1D) HEC-RAS 5.0.7 sediment model.
  • Investigation for Shoaling Reduction along the Gulf Intracoastal Waterway (GIWW) at Caney Creek, Sargent, Texas

    Purpose: This US Army Corps of Engineers (USACE) Regional Sediment Management (RSM) initiative considered alternatives for shoaling reduction in the Gulf Intracoastal Waterway (GIWW) in the vicinity of Caney Creek near Sargent, TX (Figure 1). Additionally, new beneficial use (BU) sites were considered along degraded islands adjacent to the GIWW with a threefold objective: increase the quality and quantity of habitat, reduce dredging cost via shorter pump distance, and reduce shoaling in the GIWW through East Matagorda Bay.
  • A Framework for Modeling and Assessing System Resilience Using a Bayesian Network: A Case Study of an Interdependent Electrical Infrastructure Systems Documents

    Abstract: This research utilizes Bayesian network to address a range of possible risks to the electrical power system and its interdependent networks (EIN) and offers possible options to mitigate the consequences of a disruption. The interdependent electrical infrastructure system in Washington, D.C. is used as a case study to quantify the resilience using the Bayesian net- work. Quantification of resilience is further analyzed based on different types of analysis such as forward propagation, backward propagation, sensitivity analysis, and information theory. The general insight drawn from these analyses indicate that reliability, backup power source, and resource restoration are the prime factors contributed towards enhancing the resilience of an interdependent electrical infrastructure system.
  • The Effectiveness of Laser-Induced Breakdown Spectroscopy (LIBS) as a Quantitative Tool for Environmental Characterization

    Abstract: Laser-induced breakdown spectroscopy (LIBS) is a rapid, low-cost analytical method with potential applications for quantitative analysis of soils for heavy metal contaminants found in military ranges. The Department of Defense (DoD), Army, and Department of Homeland Security (DHS) have mission requirements to acquire the ability to detect and identify chemicals of concern in the field. The quantitative potential of a commercial off-the-shelf (COTS) hand-held LIBS device and a classic laboratory bench-top LIBS system was examined by measuring heavy metals (antimony, tungsten, iron, lead, and zinc) in soils from six military ranges. To ensure the accuracy of the quantified results, we also examined the soil samples using other hand-held and bench-top analytical methods, to include Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and X-Ray Fluorescence (XRF). The effects of soil heterogeneity on quantitative analysis were reviewed with hand-held and bench-top systems and compared multivariate and univariate calibration algorithms for heavy metal quantification. In addition, the influence of cold temperatures on signal intensity and resulting concentration were examined to further assess the viability of this technology in cold environments. Overall, the results indicate that additional work should be performed to enhance the ability of LIBS as a reliable quantitative analytical tool.
  • Monitoring Ecological Restoration with Imagery Tools (MERIT): Python-based Decision Support Tools Integrated into ArcGIS for Satellite and UAS Image Processing, Analysis, and Classification

    Abstract: Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
  • A Historical Perspective on Development of Systems Engineering Discipline: A Review and Analysis

    Abstract: Since its inception, Systems Engineering (SE) has developed as a distinctive discipline, and there has been significant progress in this field in the past two decades. Compared to other engineering disciplines, SE is not affirmed by a set of underlying fundamental propositions, instead it has emerged as a set of best practices to deal with intricacies stemming from the stochastic nature of engineering complex systems and addressing their problems. Since the existing methodologies and paradigms (dominant patterns of thought and concepts) of SE are very diverse and somewhat fragmented. This appears to create some confusion regarding the design, deployment, operation, and application of SE. The purpose of this paper is (1) to delineate the development of SE from 1926-2017 based on insights derived from a histogram analysis, (2) to discuss the different paradigms and school of thoughts related to SE, (3) to derive a set of fundamental attributes of SE using advanced coding techniques and analysis, and (4) to present a newly developed instrument that could assess the performance of systems engineers. More than Two hundred and fifty different sources have been reviewed in this research in order to demonstrate the development trajectory of the SE discipline based on the frequency of publications.
  • Automated Terrain Classification for Vehicle Mobility in Off-Road Conditions

    ABSTRACT:  The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be informed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
  • Data Lake Ecosystem Workflow

    Abstract: The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer be-tween government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.
  • Evaluation of Automated Feature Extraction Algorithms Using High-resolution Satellite Imagery Across a Rural-urban Gradient in Two Unique Cities in Developing Countries

    Abstract: Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.
  • Risk-Based Prioritization of Operational Condition Assessments: Stakeholder Analysis and Literature Review

    Abstract: The US Army Corps of Engineers (USACE) operates, maintains, and manages more than $232 billion worth of the Nation’s water resource infrastructure. Using the Operational Condition Assessment (OCA) system, the USACE allocates limited resources to assess conditions and maintain assets in efforts to minimize risks associated with asset performance degradation. Currently, OCAs are conducted on each component within a facility every 5 years, regardless of the component’s risk contribution. The analysis of risks associated with Flood Risk Management (FRM) facilities, such as dams, includes considering how the facility contributes to its associated FRM watershed system, understanding the consequences of degradation in the facility’s performance, and calculating the likelihood that the facility will perform as expected given the current OCA condition ratings of critical components. This research will develop a scalable methodology to model the probability of failure of components and systems that contribute to the performance of facilities in their respective FRM systems combined with consequences derived from hydrological models of the watershed to develop facility risk scores. This interim report documents the results of the first phase of this effort, stakeholder analysis and literature review, to identify candidate approaches to determine the probability of failure of a facility.

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