20 Aug 2020

PUBLICATION NOTICE: Concept for Artificial Freezing of Sea Ice at Winter Quarters Bay, Antarctica

ABSTRACT:  McMurdo Station serves as a major research and logistics hub for the United States Antarctic Program (USAP). Adjacent to the Station is Winter Quarters Bay (WQB), where vessels dock to unload cargo and fuel. The ice pier at McMurdo is essential for this annual vessel resupply but represents a failure potential, occasionally breaking up during or immediately after vessel operations. This study aimed to determine the feasibility of using thermopiles, a passive cooling technology, to artificially freeze seawater to “grow” the existing WQB bottomfast-ice edge so that ships can dock directly against it. Finite element simulations using modeling-parameter assumptions indicate that each row of thermopiles can grow a frozen wall to a depth of 9 m in about a month if installed on 1 July with an initial sea-ice thickness of 1 m and a thermopile spacing of 1.5 m. For our simulation scenarios, we approximate that it would take 255 to 820 days to complete a 40 m by 140 m wedge of bottomfast ice. The estimated cost ranges from about $600,000 to $1,600,000. These results serve as a preliminary feasibility study of successfully using thermopiles for generating a direct docking bottomfast-ice wharf at McMurdo.

20 Aug 2020

PUBLICATION NOTICE: Sediment Sorting by Hopper Dredging and Pump-Out Operations: Sampling Methods and Analysis

Abstract: Hopper dredging operations for beach and nearshore placement typically include periods of overflow, which produces some degree of separation between the size fractions of the dredged sediment. The degree of separation and the controlling factors are presently poorly known. This report focuses on laboratory experiments aimed at determining (1) suitable sampling methods on a dredge, (2) composite sampling techniques to reduce analysis cost, (3) associated sampling intervals to achieve suitable sediment representation of a hopper load, and (4) a hydraulic means of sample splitting. Results showed that no statistical difference exists among the three methods used to sample the hopper weir overflow. The method used to sample deposited hopper sediment identified a bias in the percent fines that resulted from flow sheltering. Further, it was found that composited samples were able to quantify the concentration and percent fines accurately, although an analytical data experiment showed that the accuracy of a composited sample is dependent on the sampling intervals. The accuracy of the fines and concentration from a hydraulic sample splitter was found to be dependent on median grain size, with fine sediment being evenly distributed and coarser sediment increasing the error in concentration and grain size distribution.

20 Aug 2020

PUBLICATION NOTICE: Site-Specific Case Studies for Determining Ground Snow Loads in the United States

ABSTRACT:  The U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) has mapped ground snow loads for much of the United States. In some areas where extreme local variations preclude mapping on a national scale, instead of loads, “CS” is used to indicate that Case Studies are needed. This report and the accompanying spreadsheet, which contains the 15,104-station CRREL ground snow load database, provide the information needed to conduct Case Studies. When the latitude, longitude, and elevation of a site of interest are provided, the spreadsheet tabulates data available in the vicinity and generates plots that relate ground snow loads nearby to elevation. With this information, the ground snow load at the site of interest can be determined. This report uses 10 examples to illustrate the methodology and provides our answer and the comments we generate for each of these Case Studies and for 16 additional sites of interest, 8 of which have their answers “disguised” for practice purposes. CRREL has conducted over 1000 Case Studies upon request. Practicing structural engineers were involved in over 250 of them to verify that this methodology is ready to transfer to the design profession.

20 Aug 2020

PUBLICATION NOTICE: Understanding State-of-the-Art Material Classification through Deep Visualization

Abstract: Neural networks (NNs) excel at solving several complex, non-linear problems in the area of supervised learning. A prominent application of these networks is image classification. Numerous improvements over the last few decades have improved the capability of these image classifiers. However, neural networks are still a black-box for solving image classification and other sophisticated tasks. A number of experiments conducted look into exactly how neural networks solve these complex problems. This paper dismantles the neural network solution, incorporating convolution layers, of a specific material classifier. Several techniques are utilized to investigate the solution to this problem. These techniques look at specifically which pixels contribute to the decision made by the NN as well as a look at each neuron’s contribution to the decision. The purpose of this investigation is to understand the decision-making process of the NN and to use this knowledge to suggest improvements to the material classification algorithm.