Publication Notices

Notifications of New Publications Released by ERDC

Contact Us

      

  

    866.362.3732

   601.634.2355

 

ERDC Library Catalog

Not finding what you are looking for? Search the ERDC Library Catalog

Results:
Category: Technology
Clear
  • State of the practice in pavement structural design/analysis codes relevant to airfield pavement design

    Abstract: An airfield pavement structure is designed to support aircraft live loads for a specified pavement design life. Computer codes are available to assist the engineer in designing an airfield pavement structure. Pavement structural design is generally a function of five criteria: the pavement structural configuration, materials, the applied loading, ambient conditions, and how pavement failure is defined. The two typical types of pavement structures, rigid and flexible, provide load support in fundamentally different ways and develop different stress distributions at the pavement – base interface. Airfield pavement structural design is unique due to the large concentrated dynamic loads that a pavement structure endures to support aircraft movements. Aircraft live loads that accompany aircraft movements are characterized in terms of the load magnitude, load area (tire-pavement contact surface), aircraft speed, movement frequency, landing gear configuration, and wheel coverage. The typical methods used for pavement structural design can be categorized into three approaches: empirical methods, analytical (closed-form) solutions, and numerical (finite element analysis) approaches. This article examines computational approaches used for airfield pavement structural design to summarize the state-of-the-practice and to identify opportunities for future advancements. United States and non-U.S. airfield pavement structural codes are reviewed in this article considering their computational methodology and intrinsic qualities.
  • Hydraulic dike effects investigation on the Mississippi River: Natchez to Baton Rouge

    Abstract: This report documents an investigation of the hydraulic effects of dikes on water levels in the Mississippi River between Natchez, MS, and Baton Rouge, LA, conducted for the U.S. Army Corps of Engineers, Mississippi Valley Division, Vicksburg, MS. The investigation was conducted using a previously calibrated Natchez-to-Baton Rouge Adaptive Hydraulics numerical model. The objectives were to alter roughness and height variables associated with the dikes and overbanks encompassed in the numerical model and evaluate their effects on water surface elevations. This academic exercise provides an indication of the relative level of impact associated with modifications to the dikes and overbanks for this portion of the Mississippi River and does not represent future plans or recommendations by the U.S. Army Corps of Engineers. Steady flow simulations were simulated for 12 May 2011 to investigate the variation in model results during the peak of the 2011 flood on the Mississippi River.
  • 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.
  • Evaluation of Unmanned Aircraft System Coastal Data Collection and Horizontal Accuracy: A Case Study at Garden City Beach, South Carolina

    Abstract: The US Army Corps of Engineers (USACE) aims to evaluate unmanned aircraft system (UAS) technology to support flood risk management applications, examining data collection and processing methods and exploring potential for coastal capabilities. Foundational evaluation of the technology is critical for understanding data application and determining best practices for data collection and processing. This study demonstrated UAS Multispectral (MS) and Red Green Blue (RGB) image efficacy for coastal monitoring using Garden City Beach, South Carolina, as a case study. Relative impacts to horizontal accuracy were evaluated under varying field scenarios (flying altitude, viewing angle, and use of onboard Real-Time Kinematic–Global Positioning System), level of commercial off-the-shelf software processing precision (default optimal versus high or low levels) and processing time, and number of ground control points applied during postprocessing (default number versus additional points). Many data sets met the minimum horizontal accuracy requirements designated by USACE Engineering Manual 2015. Data collection and processing methods highlight procedures resulting in high resolution UAS MS and RGB imagery that meets a variety of USACE project monitoring needs for site plans, beach renourishment and hurricane protection projects, project conditions, planning and feasibility studies, floodplain mapping, water quality analysis, flood control studies, emergency management, and ecosystem restoration.
  • Microbiological Indicators Reflect Patterns of Life

    Abstract:  Resolving patterns of human movement, specifically for actors of interest, in an urban environment is an extremely challenging problem because of the dynamic nature of human movement. This research effort explores a highly unconventional approach, addressing residual or lingering signatures of interest to the Army in an urban operation. Research suggests that unconventional signatures commonly associated with human presence or prior occupation of a space, such as microbes attached to skin cells or in the gut, may linger for an extended amount of time. In this scoping study, our objectives were to detect microbial communities in the built environment, to examine microbial community composition, and to investigate the longevity of a microbial signature. To do so, we conducted a controlled study to obtain a mechanistic understanding of the fidelity of the biological signatures in the built environment, with a particular focus on their longevity and stability.
  • Barriers to Innovation in USACE

    Abstract: The Dredging Operations and Environmental Research Program (DOER) of the United States Army Corps of Engineers (USACE) develops new tools and practices to support the efficiency, effectiveness, and sustainability of navigation dredging operations and then implements these new approaches (that is, innovations).We analyzed the innovation process to increase the adoption and implementation of new approaches and techniques. We then created a literature review of innovation diffusion theories and developed a mental model that identifies the actual and perceived barriers to innovation diffusion in USACE through a case study of its Navigation Program. We built the final expert mental model using interviews with 25 subject matter experts familiar with the program’s processes and external stakeholders. Interviewees reported environmental and budgetary constraints, time restrictions, and politics as the most common barriers to dredging innovation, including those based on the perceptions and beliefs of stakeholders rather than hard engineering or policy constraints (herein cognitive barriers). We suggest overcoming these barriers through changes in communication channels and social systems, such as public outreach through social media channels; interpersonal face-to-face meetings with decision makers; internal collaboration between local USACE districts and external collaboration with outside stakeholders, such as contractors and environmental regulators.
  • Method Selection Framework for the Quantitation of Nanocarbon Scientific Operating Procedure Series (SOP-C-3): Selection of Methods for Release Testing and Quantitation of Solids, Suspensions, and Air Samples for Carbon-Based Nanomaterials

    Abstract: There is significant concern regarding the health and safety risk of nanocarbon (for example, nanotubes, graphene, fullerene), and the cur-rent capability gap for accurately determining exposure levels encumbers risk assessment, regulatory decisions, and commercialization. Given the various analytical challenges associated with the detection and quantitation of nanocarbon, it is unlikely that a single method or technique will prove effective for all forms of nanocarbon, all exposure scenarios, or all possible environmental systems. The optimal approach, or series of techniques, will likely depend on the nature of the material being measured, its concentration, and the matrix in which it is contained. In this work, a preliminary decision framework is presented that assists the user in deter-mining which analytical methods are best suited for a given sample.