Introduction [to special issue (v.25:5, July 2010)of the CACAIE journal, devoted to some recent research applying CI in structural engineering and mechanics]

No abstract

NASA scientific and technical publications: A catalog of special publications, reference publications, conference publications, and technical papers, 1989

This catalog lists 190 citations of all NASA Special Publications, NASA Reference Publications, NASA Conference Publications, and NASA Technical Papers that were entered into the NASA scientific and technical information database during accession year 1989. The entries are grouped by subject category. Indexes of subject terms, personal authors, and NASA report numbers are provided

Novel anisotropic continuum-discrete damage model capable of representing localized failure of massive structures. Part II: identification from tests under heterogeneous stress field

In Part I of this paper we have presented a simple model capable of describing the localized failure of a massive structure. In this part, we discuss the identification of the model parameters from two kinds of experiments: a uniaxial tensile test and a three-point bending test. The former is used only for illustration of material parameter response dependence, and we focus mostly upon the latter, discussing the inverse optimization problem for which the specimen is subjected to a heterogeneous stress field.Comment: 18 pages, 12 figures, 6 table

Expert systems and finite element structural analysis - a review

Finite element analysis of many engineering systems is practised more as an art than as a science . It involves high level expertise (analytical as well as heuristic) regarding problem modelling (e .g. problem specification,13; choosing the appropriate type of elements etc .), optical mesh design for achieving the specified accuracy (e .g . initial mesh selection, adaptive mesh refinement), selection of the appropriate type of analysis and solution13; routines and, finally, diagnosis of the finite element solutions . Very often such expertise is highly dispersed and is not available at a single place with a single expert. The design of an expert system, such that the necessary expertise is available to a novice to perform the same job even in the absence of trained experts, becomes an attractive proposition. 13; In this paper, the areas of finite element structural analysis which require experience and decision-making capabilities are explored . A simple expert system, with a feasible knowledge base for problem modelling, optimal mesh design, type of analysis and solution routines, and diagnosis, is outlined. Several efforts in these directions, reported in the open literature, are also reviewed in this paper

SciTech News Volume 70, No. 4 (2016)

Columns and Reports From the Editor 3 Division News Science-Technology Division 4 SLA Annual Meeting 2016 Report (S. Kirk Cabeen Travel Stipend Award recipient) 6 Reflections on SLA Annual Meeting (Diane K. Foster International Student Travel Award recipient) 8 SLA Annual Meeting Report (Bonnie Hilditch International Librarian Award recipient)10 Chemistry Division 12 Engineering Division 15 Reflections from the 2016 SLA Conference (SPIE Digital Library Student Travel Stipend recipient)15 Fundamentals of Knowledge Management and Knowledge Services (IEEE Continuing Education Stipend recipient) 17 Makerspaces in Libraries: The Big Table, the Art Studio or Something Else? (by Jeremy Cusker) 19 Aerospace Section of the Engineering Division 21 Reviews Sci-Tech Book News Reviews 22 Advertisements IEEE 17 WeBuyBooks.net 2

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201