10 May 2021

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.

10 May 2021

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.

10 May 2021

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.

10 May 2021

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.

10 May 2021

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.

4 May 2021

Joint Rapid Airfield Construction (JRAC) Program 2004 Demonstration Project--Fort Bragg, North Carolina

Abstract: This report describes the demonstration of technologies and procedures developed during April 2002 and May 2004 under the Joint Rapid Airfield Construction (JRAC) Program. The demonstration took place at Sicily Landing Zone (LZ) at Fort Bragg, NC, in July of 2004. The objective of the exercise was to demonstrate the procedures and technologies developed under the JRAC Program by rapidly building two parking aprons capable of supporting C-130 transport aircraft taxiing and parking operations. The exercise was conducted under continuous 24-hr operations to simulate a real-world rapid construction environment. Apron 1 (north apron) was constructed using two technologies, one-half being ACE™ Matting and the other half being a cement-polymer stabilized soil surface. Apron 2 (south apron) was constructed solely of a fiber-cement-stabilized soil system. Both aprons were treated with a polymer emulsion surface application to form a sealed surface against abrasion and water infiltration. The entire construction of both aprons required 76 hr, with Apron 1 finished in 48 hr. The construction of Apron 1 was validated by operation of a C-130 aircraft approximately 31 hr after completion with success and high praises from the aircraft flight crew on the stability and surface of the apron, as well as its dust-abating characteristics.

4 May 2021

Laboratory characterization of Cor-Tuf Baseline and UHPC-S

Abstract: This experimental effort is part of a larger program entitled Development of Ultra-High-Performance Concrete Tools and Design Guidelines. This program operates in accordance with an agreement concerning combating terrorism research and development between the United States of America Department of Defense and the Republic of Singapore Ministry of Defence. The objective of the program is to develop a better understanding of the potential benefits that may be achieved from the application of ultra-high-performance concrete (UHPC) materials for protective structures. The specific effort detailed in this report will provide insight into laboratory-scale mechanical properties of Cor-Tuf and a proprietary material termed UHPC-Singapore (UHPC-S).

4 May 2021

Study of Maintenance of High Performance Sustainable Buildings (HPSB)

Abstract: A study was performed by the Energy Branch of the US Army Engineer Research and Development Center, Construction Engineering Research Laboratory, on behalf of the US Army Installation Management Command under the Installation Technology Transition Program. The focus of the study was related to maintainability and operability issues associated with High Performance Sustainable Buildings (HPSBs). This study was conducted primarily based on information gleaned from telephone and web conference discussions with installation Directorate of Public Works personnel including Operation and Maintenance (O&M) Chiefs, energy managers, maintenance supervisors, and maintenance technicians. Experiences with HPSBs varied from installation to installation. For example, some installations had very positive experiences with photovoltaic (PV) arrays while other sites questioned their practicality due to maintainability problems. One site noted that PV technologies are changing so rapidly that procuring spare/repair parts becomes difficult or impossible when vendors discontinue supporting their older technologies or manufacturers go out of business. Based on discussions with the installation O&M personnel, a table of pro and con recommendations for 25 technologies, which are commonly implemented on HPSBs, was prepared and is included in this report.

4 May 2021

Microscale Dynamics between Dust and Microorganisms in Alpine Snowpack

ABSTRACT:  Dust particles carry microbial and chemical signatures from source regions to deposition regions. Dust and its occupying microorganisms are incorporated into, and can alter, snowpack physical properties including snow structure and resultant radiative and mechanical properties that in turn affect larger-scale properties, including surrounding hydrology and maneuverability. Microorganisms attached to deposited dust maintain genetic evidence of source substrates and can be potentially used as bio-sensors. The objective of this study was to investigate the impact of dust-associated microbial deposition on snowpack and microstructure. As part of this effort, we characterized the microbial communities deposited through dust transport, examined dust provenance, and identified the microscale location and fate of dust within a changing snow matrix. We found dust characteristics varied with deposition event and that dust particles were generally embedded in the snow grains, with a small fraction of the dust particles residing on the exterior of the snow matrix. Dust deposition appears to retard expected late season snow grain growth. Both bacteria and fungi were identified in the collected snow samples.

4 May 2021

Utilizing Data from the NOAA National Data Buoy Center

Purpose: This Coastal and Hydraulics Engineering Technical Note (CHETN) guides users through the quality control (QC) and processing steps that are necessary when using archived U.S. National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) wave and meteorological data. This CHETN summarizes methodologies to geographically clean and QC NDBC measurement data for use by the U.S. Army Corps of Engineers (USACE) user community.