Vicksburg, MS, United States
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East E.W.,Research Civil Engineer | Bogen C.,Computer Scientist
Congress on Computing in Civil Engineering, Proceedings | Year: 2012

The authors' efforts to improve the quality of Industry Foundation Class (IFC) building information exchanges has highlighted needed for defensible verification methods. The tools and techniques needed to meet these efforts requirements would also improve research that requires building information. This paper announces the open publication of a series of models and tools produced and used by the authors for their research. Widespread use of common models and shared tools are expected to improve the quality of research that requires building information. © 2012 American Society of Civil Engineers.

News Article | February 27, 2017

This excerpt is from the SPIE Press book Discrimination of Subsurface Unexploded Ordnance. Buried unexploded ordnance (UXO) poses a persistent, challenging, and expensive cleanup problem. Whether on military practice lands or at the sites of past conflicts, many dropped bombs and fired projectiles failed to explode when they penetrated the ground, thus affecting the accessibility of millions of acres at thousands of sites in the US alone. Globally, the problem is even more extensive and dauntingly diverse. The cleanup of UXO sites is particularly challenging because detection must be extraordinarily reliable and remediation extremely careful. Beyond the challenges of problematic terrain, the sheer diversity of possible ordnance types compounds the difficulties inherent in the fuzziness of what practicable sensors provide. In most locations where some ordnance did not explode, many more items have indeed detonated; clutter is abundant, and its sensor responses are often similar to comparably sized UXO. Necessarily conservative practices to date ensure an enormous false alarm rate, and thus cleanup costs are very high. Against this background, recent developments provide a heartening story; the particulars are engaging, and there is a happy ending. Spanning the last ten or fifteen years, the narrative proceeds over a continuum of all aspects of the problem: [excerpt from Chapter 1: The Problem and Its Nature] Section 1.1 Discrimination, Inverse Problems, and What Follows For the most part, one cannot make discrimination decisions by examining recorded signals alone; they typically vary greatly as a function of the sensor–object configuration, which is inherently unknown. Instead, data must be related to a model of the sensor–object interaction. It is via such a model that one can infer underlying parameters that are not just functions of individual signals. Overall, the three essential constituents of the task are: Specifically for the discrimination portion of this task, a complete system requires the following essential elements: In terms of the dynamics of development, there is a great deal of interaction amongst the first three items, such that no one of them really precedes or follows the other. Available instruments require that modelers confront specific kinds of sensor–object interactions, along with the way in which those interactions are reported. This fact effectively defines or at least constrains the modeler’s task. Models and the feasibility of extracting their parameters from data may simultaneously direct developments in instrumentation. The previous list inherently concerns inverse problems, from the most focused level (i.e., how can the field data be treated to infer the specific quantities of interest?) to the broadest level (i.e., is whatever caused this signal an UXO?). To illuminate matters in this domain, let us treat the following general equation as posing, alternatively, a forward problem, a direct inverse solution, and a general inverse problem: where an uppercase bold letter (A) indicates a matrix or tensor, with prominent exceptions to be noted. A lowercase bold letter indicates a vector, e.g., of sources q producing the vector of data d. The specifics of A derive from the relevant physics, geometrical configuration, boundary, or other conditions; and the matrix incorporates some parameters , , …. In a causal view, the entities on the left produce the observations (data, output) on the right. In the forward problem, everything on the left side of Eq. (1.1) needed to obtain the output d is known, including the source strengths q as well as all geometry, applied conditions, etc., that produce the structure and parameters in A. One need only turn the computational crank (multiplication) to produce the result. Many inversion approaches, particularly those based on optimization searches, rely on the repeated execution of forward calculations using prospective parameters and/or q values. Along with direct inverse solutions, this forward calculation is what most engineers and scientists were taught in general physics and math courses. At the outset of the direct inverse solution, A and d are known, but q is unknown. Assuming that the problem has been properly formulated, the measurements taken, and the system structured so that A has no problematic features (something of a leap, as will be seen), the relation in Eq. (1.1) can be inverted mechanically. That is, one may bring to bear a straightforward algorithm with a set of reliable, codified steps that will produce the solution q. Loosely speaking, the causes in the forward problem are known, and the result is computed; in the direct inverse solution, the result is known, and the causes are calculated. In general, it is advisable to reduce at least parts of general inversion calculations to well-posed direct solutions. As shown below, exploiting even the direct inverse calculation may be more fraught than it initially seems. A general inverse problem is distinct from the two previous calculations and is also harder to define precisely. The data d are known, at least to some degree of certainty. The essential question is: what caused this data? The form of the left side of Eq. (1.1) may not be as accommodating as in the direct inversion. Some constituents of A (e.g., , , …) may themselves be unknown and may be part of the solution being sought, in addition to q. For example, the parameters may correspond to such items as source position, material properties, or geometrical orientations. It is much more difficult to codify this kind of inverse calculation than in the other two instances. Pitfalls abound, depending on the specific formulation and the computational measures taken. Groping searches may be required, essentially guessing likely answers (left side of the equation) and performing repeated forward calculations based on those values. Analysts try to zero in on source and parameter values that work best according to some measure of agreement between calculated and observed d values, while also satisfying any conditions and constraints. It is tempting then to treat this best result as an approximation of “true” input and parameter values, but a number of issues contribute to ambiguity here. Substantially different sets of possible sources and parameters could result in reasonable approximations of the same data. Optimization searches may get stuck around local error minima. The inevitable noise and error in the system may make it unclear how well a global error minimum has been identified. Successes notwithstanding, much work remains to be done, if only because formidable settings abound, including rugged, vegetated terrain, wetlands, and underwater sites. For more information about this challenging yet vital field, read the full Tutorial Text Discrimination of Subsurface Unexploded Ordnance. -Kevin O’Neill received a B.A. magna cum laude from Cornell University and a M.A., M.S.E., and Ph.D. from Princeton University. After a National Science Foundation Postdoctoral Fellowship with the Thayer School of Engineering, Dartmouth College, and at the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL), he became a Research Civil Engineer with CRREL. His research has focused on porous media transport phenomena and geotechnically relevant electromagnetics. He has been a Visiting Fellow with the Department of Agronomy, Cornell University, and a Visiting Scientist with the Center for Electromagnetic Theory and Applications, Massachusetts Institute of Technology. Since 1984, he has been an adjunct faculty member with the Thayer School of Engineering, Dartmouth College. His current research interests include electromagnetic remote sensing of surfaces, layers, and buried objects in particular, such as unexploded ordnance.

Doyle J.D.,Research Civil Engineer | Howard I.L.,Mississippi State University | Gartrell C.A.,Research Civil Engineer | Anderton G.L.,Geotechnical and Structures Laboratory | And 2 more authors.
International Journal of Geomechanics | Year: 2014

Matting systems are used for temporary applications on soft soils to reduce ground pressure exerted by aircraft, heavy equipment, vehicles, and construction material. They have been used for military airfields, construction platforms, and similar applications. Previous evaluation studies of matting systems have typically consisted of full-scale testing, with only a limited amount of numerical modeling found in the literature. This paper presents results of full-scale accelerated testing of 21 test sections encompassing five matting systems, five soil-support conditions, and two aircraft loadings. One of the soil-support conditions was instrumented and tested in conjunction with three matting systems and one aircraft loading. Three-dimensional finite-element modeling was performed on the instrumented sections using the measured test data for calibration. Good matches of measured soil stresses were obtained with the model for two of the mats, whereas the model underpredicted stresses in the third mat. Modeling of the type performed in this paper was capable of correctly ranking the performance of the matting systems modeled relative to the full-scale test results. © 2014 American Society of Civil Engineers.

Kayen R.,Research Civil Engineer | Kayen R.,University of California at Los Angeles | Moss R.E.S.,California Polytechnic State University, San Luis Obispo | Thompson E.M.,Tufts University | And 5 more authors.
Journal of Geotechnical and Geoenvironmental Engineering | Year: 2013

Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand and the soil capacity were estimated and included in the analysis, resulting in greatly reduced overall model uncertainty relative to previous studies. The presented data set and probabilistic analysis also help resolve the ancillary issues of adjustment for soil fines content and magnitude scaling factors. © 2013 American Society of Civil Engineers.

Doyle J.D.,Mississippi State University | Mejias-Santiago M.,Research Civil Engineer | Brown E.R.,Research Civil Engineer | Howard I.L.,Mississippi State University
Asphalt Paving Technology: Association of Asphalt Paving Technologists-Proceedings of the Technical Sessions | Year: 2011

This paper presents test results on warm mix asphalt (WMA) mixtures containing high amounts of reclaimed asphalt pavement (RAP) to evaluate performance when used as a surface mixture. The evaluation considered permanent deformation, durability, non-load associated cracking, and moisture damage. Crushed gravel and limestone mixtures were tested with 0 to 50% RAP in conjunction with three warm mix asphalts. Test results indicated high RAP-WMA to be a potentially viable product for surface mixtures. WMA was shown capable of producing rut resistant mixtures with high RAP contents. A more intriguing finding was that while increasing rut resistance the high RAP mixtures did not affect the low temperature properties as much as the high temperature properties. Mixtures with high RAP content appear to be only slightly more susceptible to thermal cracking. Warm mixes have been shown to be susceptible to water. In general it was shown that WMA technology can be used with high RAP content to produce mixtures that are more resistant to moisture damage. Increasing the RAP content generally increased the resistance of the mix to moisture damage. Gravel mixtures produced with foamed asphalt, however, did not perform well with respect to moisture damage, especially with no RAP in the mixture. Testing indicated high RAP content WMA mixes may be more susceptible to durability issues than low RAP mixes. To date this has not been observed in the field, but additional monitoring of high RAP sections is needed to determine if and to what extent durability on the surface is a cause of concern.

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