Application of artificial neural networks in nonlinear analysis of trusses

A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods

The method of lines in three dimensional fracture mechanics

A review of recent developments in the calculation of design parameters for fracture mechanics by the method of lines (MOL) is presented. Three dimensional elastic and elasto-plastic formulations are examined and results from previous and current research activities are reported. The application of MOL to the appropriate partial differential equations of equilibrium leads to coupled sets of simultaneous ordinary differential equations. Solutions of these equations are obtained by the Peano-Baker and by the recurrance relations methods. The advantages and limitations of both solution methods from the computational standpoint are summarized

Singularities in optimal structural design

Singularity conditions that arise during structural optimization can seriously degrade the performance of the optimizer. The singularities are intrinsic to the formulation of the structural optimization problem and are not associated with the method of analysis. Certain conditions that give rise to singularities have been identified in earlier papers, encompassing the entire structure. Further examination revealed more complex sets of conditions in which singularities occur. Some of these singularities are local in nature, being associated with only a segment of the structure. Moreover, the likelihood that one of these local singularities may arise during an optimization procedure can be much greater than that of the global singularity identified earlier. Examples are provided of these additional forms of singularities. A framework is also given in which these singularities can be recognized. In particular, the singularities can be identified by examination of the stress displacement relations along with the compatibility conditions and/or the displacement stress relations derived in the integrated force method of structural analysis

Structural analysis consultation using artificial intelligence

The primary goal of consultation is definition of the best strategy to deal with a structural engineering analysis objective. The knowledge base to meet the need is designed to identify the type of numerical analysis, the needed modeling detail, and specific analysis data required. Decisions are constructed on the basis of the data in the knowledge base - material behavior, relations between geometry and structural behavior, measures of the importance of time and temperature changes - and user supplied specifics characteristics of the spectrum of analysis types, the relation between accuracy and model detail on the structure, its mechanical loadings, and its temperature states. Existing software demonstrated the feasibility of the approach, encompassing the 36 analysis classes spanning nonlinear, temperature affected, incremental analyses which track the behavior of structural systems

Using monoclonal antibodies to label living root hairs: a novel tool for studying cell wall microarchitecture and dynamics in <i>Arabidopsis</i>

Background&lt;p&gt;&lt;/p&gt; The Arabidopsis root hair represents a valuable cell model for elucidating polar expansion mechanisms in plant cells and the overall biology of roots. The deposition and development of the cell wall is central to the root hair expansion apparatus. During this process, incorporation of specific wall polymers into the growing wall architecture constitutes a critical spatio-temporal event that controls hair size and growth rate and one that is closely coordinated with the cell’s endomembrane, cytoskeletal and signal transduction apparatuses.&lt;p&gt;&lt;/p&gt; Results&lt;p&gt;&lt;/p&gt; In this study, the protocol for live cell labeling of roots with monoclonal antibodies that bind to specific wall polymers is presented. This method allows for rapid assessment of root hair cell wall composition during development and assists in describing changes to cell wall composition in transgenic mutant lines. Enzymatic “unmasking” of specific polymers prior to labeling allows for refined interpretation of cell wall chemistry. Live cell immunofluorescence data may also be correlated with transmission electron microscopy-based immunogold labeling.&lt;p&gt;&lt;/p&gt; Conclusions&lt;p&gt;&lt;/p&gt; Live Arabidopsis root hairs may be labeled with cell wall polymer-specific antibodies. This methodology allows for direct visualization of cell wall dynamics throughout development in stable transgenic plant lines. It also provides an important new tool in the elucidation of the specific interactions occurring between membrane trafficking networks, cytoskeleton and the cell wall deposition/remodeling mechanism

Presidential Administration in a Regime of Separated Powers: An Analysis of Recent American Experience

This Article examines presidential direction of administrative action in the Obama and early Trump Administrations against the backdrop of ongoing debates concerning: (i) the desirability of and appropriate techniques for presidential control of administration and (ii) the relevance of separated powers when American government is under unified political control. To give this analysis a concrete context, the Article provides in-depth case studies of presidential administration in immigration policy, climate change policy, and executive structuring of the administrative state, under both the Obama and early Trump Administrations

Material Data Representation of Hysteresis Loops for Hastelloy X Using Artificial Neural Networks

The artificial neural network (ANN) model proposed by Rumelhart, Hinton, and Williams is applied to develop a functional approximation of material data in the form of hysteresis loops from a nickel-base superalloy, Hastelloy X. Several different ANN configurations are used to model hysteresis loops at different cycles for this alloy. The ANN models were successful in reproducing the hysteresis loops used for its training. However, because of sharp bends at the two ends of hysteresis loops, a drift occurs at the corners of the loops where loading changes to unloading and vice versa (the sharp bends occurred when the stress-strain curves were reproduced by adding stress increments to the preceding values of the stresses). Therefore, it is possible only to reproduce half of the loading path. The generalization capability of the network was tested by using additional data for two other hysteresis loops at different cycles. The results were in good agreement. Also, the use of ANN led to a data compression ratio of approximately 22:1