Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis--University of Missouri, School of Mines and Metallurgy, 1876. / J.E. McGrath determined to be James E. McGrath from "1874-1990 MSM-UMR Alumni Directory". The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed August 18, 2008)

No description available.

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Metal-plate-connected (MPC) splice joints were tested in combined tension and bending to generate data that were used in the development of a design procedure for determining the steel net-section strength of bottom chord splice joints of MPC wood trusses. Several common wood truss splice joint configurations were tested at varying levels of combined tension and bending loading. The joint configurations were 2x4 lumber with 20-gauge truss plates, 2x6 lumber with 20-gauge truss plates, and 2x6 lumber with 16-gauge truss plates. All the joints tested failed in the steel net-section of the truss plates. The combined loading was achieved by applying an eccentric axial tension load to the ends of each splice joint specimen. Three structural models were developed to predict the ultimate strength of the steel net-section of the splice joints tested under combined tension and bending loading. The test data were fitted to each model, and the most accurate model was selected. Data from other published tests of splice joints were used to validate the accuracy of the selected model. A design procedure for determining the allowable design strength of the steel net-section of a splice joint subjected to combined tension and bending was developed based on the selected model. The new design procedure was compared with two existing design methods. The proposed design procedure is recommended for checking the safe capacity of the steel net-section of bottom chord splice joints of MPC wood trusses subjected to combined tension and bending. / Master of Science

Project report (M. Eng.)--Virginia Polytechnic Institute and State University, 1983. / Vita. Abstract. Includes bibliographical references (leaves 75-81). Also available via the Internet.

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

Automated systems for large-span roof truss optimization provide engineers with the flexibility to consider multiple alternatives during conceptual design. This investigation extends previous work on multi-objective roof truss optimization to include the design preferences of a human user. The incorporation of user preferences into the optimization process required creation of a mechanism to identify and model preferences as well as discovery of an appropriate location within the algorithm for preference application. The first stage of this investigation developed a characteristic feature vector to describe the physical appearance of an individual truss. The feature vector translates visual elements of a truss into quantifiable properties transparent to the computer algorithm. The nine elements in the feature vector were selected from an assortment of geometrical and behavioral factors and describe truss simplicity, general shape, and chord shape. Using individual feature vectors, a truss population may be divided into groups of similar design. Partitioning the population simplifies the feedback process by allowing users to identify groups that best suit their design preferences. Several unsupervised clustering mechanisms were evaluated for their ability to generate truss classifications that matched human judgment and minimized intra-group deviation. A one-dimensional Kohonen self-organizing map was selected. The characteristic feature vectors of truss designs within user-selected groups provided a basis for determining whether or not a user would like a new design. After analyzing user inputs, prediction algorithm trials sought to reproduce these inputs and apply them to the prediction of acceptable designs. This investigation developed a hybrid method combining rough set reduct techniques and a back-propagation neural network. This hybrid prediction mechanism was embedded into the operations of an Implicit Redundant Representation Genetic Algorithm. Locations within the ranking and selection processes of this algorithm formed the basis of a study to investigate the effect of user preference on truss optimization. Final results for this investigation prove that incorporating a user's aesthetic design preferences into the optimization project generates more design alternatives for the user to examine; that these alternatives are more in line with a user's conceptual perception of the project; and that these alternatives remain structurally optimal.

This thesis describes the experimental and numerical investigation of a novel cold-formed steel 48ft and 54ft long span truss. The truss we designed was to be used as the roofs of large buildings, such as warehouses, hangars, sports arenas. The investigation includes both experimental and numerical testing, the experimental testing of the truss under uniform loads (increasing loads) to determine its deflection and load carrying capacity. The numerical test included developing a finite element model of the truss in SolidWorks and using a finite element model of the truss in ABAQUS to simulate the experimental tests. The findings of this study can be used to improve the design of cold-formed steel long span trusses. The study also provides valuable information for future studies on the modeling of trusses with different cold-formed steel members and the behavior of trusses under load.