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  • Holistic and Reductionist Thinker: A Comparison Study Based on Individuals’ Skillset and Personality Types

    Abstract: As organizations operate in turbulent and complex environments, it has become a necessity to assess the systems thinking (ST) skills, personality types (PTs), and demographics of practitioners. In this study, we investigated the relationship between practitioners’ ST profile, their PTs profiles and demographic characteristics in the domain of complex system problems. The objective of this study is to address the current gap in the literature – lack of studies dedicated to predicting practitioners’ ST profile based on their PTs and demographics characteristics. A total of 258 practitioners with different demographics and PTs provided the data. The results show that (1) practitioners can be classified based on their ST skills scores into two clusters: holistic and reductionist (that is, ST profile), (2) each cluster has different PTs profiles and demographic characteristics, and (3) practitioner’s ST profile can be predicted, with good accuracy, based on their PTs profile and demographic characteristics.
  • Effect of Individual Differences in Predicting Engineering Students' Performance: A Case of Education for Sustainable Development

    Abstract: The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.
  • Field Survey to Prioritize Needs for Modernizing Dredged Material Evaluation Guidance

    Abstract: This technical note synthesizes and disseminates results of a 2020 survey of USACE dredging program and project managers to identify and prioritize needs related to the modernization and streamlining of the dredged material assessment decision guidance pursuant to Section 404 of the Clean Water Act (CWA) and Section 103 of the Marine Protection Research and Sanctuaries Act (MPRSA). Priorities identified through the survey and subsequent follow-on interviews—together with advances in science and technology—will facilitate development of an electronic decision guidance tool to enable consistent, timely, and cost-effective dredged material management decisions. This tool will also facilitate a standardized database for ready access to historical data.
  • Vertical and slanted sound propagation in the near-ground atmosphere: amplitude and phase fluctuations

    ABSTRACT: Sound propagation along vertical and slanted paths through the near-ground atmosphere impacts detection and localization of low-altitude sound sources, such as small unmanned aerial vehicles, from ground-based microphone arrays. This article experimentally investigates the amplitude and phase fluctuations of acoustic signals propagating along such paths. The experiment involved nine microphones on three horizontal booms mounted at different heights to a 135-m meteorological tower at the National Wind Technology Center (Boulder, CO). A ground-based loudspeaker was placed at the base of the tower for vertical propagation or 56 m from the base of the tower for slanted propagation. Phasor scatterplots qualitatively characterize the amplitude and phase fluctuations of the received signals during different meteorological regimes. The measurements are also compared to a theory describing the log-amplitude and phase variances based on the spectrum of shear and buoyancy driven turbulence near the ground. Generally, the theory correctly predicts the measured log-amplitude variances, which are affected primarily by small-scale, isotropic turbulent eddies. However, the theory overpredicts the measured phase variances, which are affected primarily by large-scale, anisotropic, buoyantly driven eddies. Ground blocking of these large eddies likely explains the overprediction.
  • Performance of Active Porcelain Enamel Coated Fibers for Fiber-Reinforced Concrete: The Performance of Active Porcelain Enamel Coatings for Fiber-Reinforced Concrete and Fiber Tests at the University of Louisville

    Abstract: A patented active porcelain enamel coating improves both the bond between the concrete and steel reinforcement as well as its corrosion resistance. A Small Business Innovation Research (SBIR) program to develop a commercial method for production of porcelain-coated fibers was developed in 2015. Market potential of this technology with its steel/concrete bond improvements and corrosion protection suggests that it can compete with other fiber reinforcing systems, with improvements in performance, durability, and cost, especially as compared to smooth fibers incorporated into concrete slabs and beams. Preliminary testing in a Phase 1 SBIR investigation indicated that active ceramic coatings on small diameter wire significantly improved the bond between the wires and the concrete to the point that the wires achieved yield before pullout without affecting the strength of the wire. As part of an SBIR Phase 2 effort, the University of Louisville under contract for Ceramics, Composites and Coatings Inc., proposed an investigation to evaluate active enamel-coated steel fibers in typical concrete applications and in masonry grouts in both tension and compression. Evaluation of the effect of the incorporation of coated fibers into Ultra-High Performance Concrete (UHPC) was examined using flexural and compressive strength testing as well as through nanoindentation.
  • Suppressing the pressure-source instability in modeling deep-draft vessels with low under-keel clearance in FUNWAVE-TVD

    Abstract: This Coastal and Hydraulics Engineering Technical Note (CHETN) documents the development through verification and validation of three instability-suppressing mechanisms in FUNWAVE-TVD, a Boussinesq-type numerical wave model, when modeling deep-draft vessels with a low under-keel clearance (UKC). Many large commercial ports and channels (e.g., Houston Ship Channel, Galveston, US Army Corps of Engineers [USACE]) are traveled and affected by tens of thousands of commercial vessel passages per year. In a series of recent projects undertaken for the Galveston District (USACE), it was discovered that when deep-draft vessels are modeled using pressure-source mechanisms, they can suffer from model instabilities when low UKC is employed (e.g., vessel draft of 12 m¹ in a channel of 15 m or less of depth), rendering a simulation unstable and obsolete. As an increasingly large number of deep-draft vessels are put into service, this problem is becoming more severe. This presents an operational challenge when modeling large container-type vessels in busy shipping channels, as these often will come as close as 1 m to the bottom of the channel, or even touch the bottom. This behavior would subsequently exhibit a numerical discontinuity in a given model and could severely limit the sample size of modeled vessels. This CHETN outlines a robust approach to suppressing such instability without compromising the integrity of the far-field vessel wave/wake solution. The three methods developed in this study aim to suppress high-frequency spikes generated nearfield of a vessel. They are a shock-capturing method, a friction method, and a viscosity method, respectively. The tests show that the combined shock-capturing and friction method is the most effective method to suppress the local high-frequency noises, while not affecting the far-field solution. A strong test, in which the target draft is larger than the channel depth, shows that there are no high-frequency noises generated in the case of ship squat as long as the shock-capturing method is used.
  • A Computational Prototyping Environment Interface for DoD CREATETM-AV Helios Simulations

    Abstract: Computational Prototyping Environment (CPE) is a web-based portal designed to simplify running Department of Defense (DoD) modeling and simulation tools on the DoD Supercomputing Resource Center’s (DSRC) High Performance Computing (HPC) systems. The first of these tools to be deployed in the CPE is an application (app) to conduct parametric studies and view results using the CREATE-AV Helios CFD software. Initial capability includes hover (collective sweep) and forward flight (speed sweep) performance calculations. The CPE Helios app allows for job submission to a DSRC’s HPC system and for the viewing of results created by Helios, i.e., time series and volumetric data. Example data input and results viewing are presented. Planned future functionality is also outlined.
  • 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.
  • Automated Characterization of Ridge-Swale Patterns Along the Mississippi River

    Abstract: The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from other features, such as the levee structure and riverbanks. Further tuning of training data or manual identification of regions of interest could yield significantly better results. The workflow also provides an orientation to each linear feature to support subsequent analyses of position relative to levee alignments. While the individual models fall short of immediate applicability, the procedure provides a feasible, automated scheme to assist in swale classification and characterization within mature alluvial valley systems similar to LMV.
  • Methodology for Remote Assessment of Pavement Distresses from Point Cloud Analysis

    Abstract: The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.