Thesis (M.A.)--York University, 2004. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 146-156). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: LINK NOT YET AVAILABLE.

Aging railroad bridge infrastructure is subject to increasingly higher demands such as heavier loads, increased speed, and increased frequency of traffic. The challenges facing railroad bridge infrastructure provide an opportunity to develop improved systems of monitoring railroad bridges. This dissertation outlines the development and implementation of a Structural Impairment Detection System (SIDS) that incorporates finite element modeling and instrumentation of a testbed structure, neural algorithm development, and the integration of data acquisition and impairment detection tools. Ultimately, data streams from the Salmon Bay Bridge are autonomously recorded and interrogated by competitive arrays of artificial neural networks for patterns indicative of specific structural impairments. Heel trunnion bascule bridges experience significant stress ranges in critical truss members. Finite element modeling of the Salmon Bay Bridge testbed provided an estimate of nominal structural behavior and indicated types and locations of possible impairments. Analytical modeling was initially performed in SAP2000 and then refined with ABAQUS. Modeling results from the Salmon Bay Bridge were used to determine measureable quantities sensitive to modeled impairments. An instrumentation scheme was designed and installed on the testbed to record these diagnostically significant data streams. Analytical results revealed that main chord members and bracing members of the counterweight truss are sensitive to modeled structural impairments. Finite element models and experimental observations indicated maximum stress ranges of approximately 22 ksi on main chord members of the counterweight truss. A competitive neural algorithm was developed to examine analytical and experimental data streams. Analytical data streams served as training vectors for training arrays of competitive neural networks. A quasi static array of neural networks was developed to provide an indication of the operating condition at specific intervals of the bridge's operation. Competitive neural algorithms correctly classified 94% of simulated data streams. Finally, a stand-alone application was integrated with the Salmon Bay Bridge data acquisition system to autonomously analyze recorded data streams and produce bridge condition reports. Based on neural algorithms trained on modeled impairments, the Salmon Bay Bridge operates in a manner most resembling one of two operating conditions: 1) unimpaired, or 2) impaired embedded member at the southeast corner of the counterweight.

This research introduces neural networks (NN) as a novel approach towards aiding biocomposite materials processing. At its core, the aim of the research was to investigate NN usage as a tool for advancing the field of biocomposites. Empirical data was generated for compression-molded flax fiber and High Density Polyethylene (HDPE) matrix based biocomposite materials. In an attempt to create the NN model, tensile strength, impact strength, hardness, bending strength, and density were provided to the NN as inputs. These inputs were processed through multiple layers of the NN, and contributed to the prediction of the composition (fiber loading percentage) and operating parameter (pressure in MPa) as output. In précis, NNs use was investigated to predict composition and operational parameter for biocomposites production when the desired mechanical properties of the biocomposites were available. Flax (Linum usitatissimum) fiber biocomposite boards were manufactured using chemically pretreated flax fiber and high density polyethylene (HDPE). After extensive preprocessing (combing and size reduction to 2 mm particles) and pretreatment regimen - flax fiber was mixed with HDPE and extruded using a laboratory scale single screw extruder. Extrudates generated from the extruder were again ground to 2 mm particles. Ground extrudates from different sample sets were exposed to a compression molding unit. The mold was put under two sets of pressures, (variable operating parameters) for all individual fiber loading. These boards were used to determine the mechanical properties tensile force, impact force, hardness, bending, and density. For verification and analysis of the mechanical properties, Microsoft Office Excel and a statistical software package SAS were used. After verification five different multilayer neural networks, i.e., cascade forward neural network, feedforward backpropagation neural network, neural unit (single layer, single neuron), feedforward time delay neural network and NARX, were trained and evaluated for performance. Ultimately, the feedforward backpropagation NN (FFBPNN) was selected as the most efficient. After rigorous testing, the FFBPNN trained by the TRAINSCG algorithm (Matlab ®) was selected to generate prediction results that were the most suitable, fast and accurate. Once the selection and training of the NN architecture was complete, biocomposite materials prediction was performed. From 9 separate input sets, NNs provided overall prediction error between 2 - and 4%. This was the same amount of error that was observed in the training of the neural network. It was concluded that the neural network approach for the experimental design and operational conditions were satisfied.

This research introduces neural networks (NN) as a novel approach towards aiding biocomposite materials processing. At its core, the aim of the research was to investigate NN usage as a tool for advancing the field of biocomposites. Empirical data was generated for compression-molded flax fiber and High Density Polyethylene (HDPE) matrix based biocomposite materials. In an attempt to create the NN model, tensile strength, impact strength, hardness, bending strength, and density were provided to the NN as inputs. These inputs were processed through multiple layers of the NN, and contributed to the prediction of the composition (fiber loading percentage) and operating parameter (pressure in MPa) as output. In précis, NNs use was investigated to predict composition and operational parameter for biocomposites production when the desired mechanical properties of the biocomposites were available. Flax (Linum usitatissimum) fiber biocomposite boards were manufactured using chemically pretreated flax fiber and high density polyethylene (HDPE). After extensive preprocessing (combing and size reduction to 2 mm particles) and pretreatment regimen - flax fiber was mixed with HDPE and extruded using a laboratory scale single screw extruder. Extrudates generated from the extruder were again ground to 2 mm particles. Ground extrudates from different sample sets were exposed to a compression molding unit. The mold was put under two sets of pressures, (variable operating parameters) for all individual fiber loading. These boards were used to determine the mechanical properties tensile force, impact force, hardness, bending, and density. For verification and analysis of the mechanical properties, Microsoft Office Excel and a statistical software package SAS were used. After verification five different multilayer neural networks, i.e., cascade forward neural network, feedforward backpropagation neural network, neural unit (single layer, single neuron), feedforward time delay neural network and NARX, were trained and evaluated for performance. Ultimately, the feedforward backpropagation NN (FFBPNN) was selected as the most efficient. After rigorous testing, the FFBPNN trained by the TRAINSCG algorithm (Matlab ®) was selected to generate prediction results that were the most suitable, fast and accurate. Once the selection and training of the NN architecture was complete, biocomposite materials prediction was performed. From 9 separate input sets, NNs provided overall prediction error between 2 - and 4%. This was the same amount of error that was observed in the training of the neural network. It was concluded that the neural network approach for the experimental design and operational conditions were satisfied.

Many parts used by the automobile manufacturers are provided by outside suppliers. Hence, the chain between the automobile manufacturers and their suppliers has been considered very important for the purchasing department of an automobile factory. Finding qualified suppliers that can meet the demands of the automobile manufacturers is thus an important issue. With the application of neural networks, this thesis develops an approach to help determining the qualification of the suppliers. By using data of the known qualified and unqualified suppliers and by setting a number of features to characterize the capability of the suppliers, neural networks are trained to determine the qualification of the suppliers. In training the neural networks, the features are incrementally removed until optimal classification accuracy is reached. It is hoped that this system can become an effective decision-supporting system in screening the potential suppliers for the automobile manufacturers.

Neural Networks

Liquefied Natural Gas (LNG) is becoming an increasing factor in the U.S. natural gas market. For 30 years LNG imports into the U.S. have remained fairly flat. There are currently 18 permit applications being filed in the U.S. and another 10 permit applications being filed in Canada and Mexico for LNG import terminals. The EIA (Energy Information Agency) estimates by 2025 that LNG will make up 21% of the total U.S. Natural Gas Supply. This study developed a neural network approach to forecast LNG imports into the U.S. Various input variables were gathered, organized into groups based on similarity, and then a correlation matrix was generated to screen out redundant variables. Since a limited number of data points were available I used a restricted number of input variables. Based on this restriction, I grouped the input variables into four different scenarios and then generated a forecast for each scenario. These four different scenarios were the $/MMBTU model, natural gas energy consumption model, natural gas consumption model and the energy stack model. The standard neural network approach was also used to screen the input variables. First, a correlation matrix determined which variables had a high correlation with the output, U.S. LNG imports. The ten most correlated input variables were then put into correlation matrix to determine if there were any redundant variables. Due to the lack of data points only the five most highly correlated input variables were used in the neural network simulation. A number of interesting results were obtained from this study. The energy stack model and the consumption of natural gas forecasted a non-linear trend in U.S. LNG imports, compared to the linear trend forecasted by the EIA. The energy stack model and consumption of natural gas model predicted that in 2025 U.S. LNG imports will be about 6.5 TCF, while the other three models prediction is about three times as less. The energy stack model is the most realistic model due its non-linear trend, when the rapid increase of LNG imports is going to occur, and the quantity of U.S. LNG imports predicted in 2025.

The work described in this thesis concerns the study of the evolution of simple forms of learning behaviour in artificial agents. Our interest in the phylogeny of learning has been developed within the theoretical framework provided by the "ecological approach" to the study of learning. The latter is a recent theoretical and methodological perspective which, contrary to that suggested by the classical approaches in animal and comparative psychology, has reconsidered the importance of the evolutionary analysis of learning as a species- niche-specific adaptive process, which should be investigated by employing the conceptual apparatus originally developed by J. J. Gibson within the context of visual perception. However, it has been acknowledged in the literature that methodological difficulties are hindering the evolutionary ecological study of learning. We argue that methodological tools - i. e., artificial agent based models - recently developed within the context of biologically-oriented cognitive science can potentially represent a complementary methodology to investigate issues concerning the evolutionary history of learning without losing sight of the complexity of the ecological perspective. Thus, the experimental work presented in this thesis contributes to the discussion on the adaptive significance of learning, through the analysis of the evolution of simple forms of associative learning in artificial agents. Part of the work of the thesis focuses on the study of the nature of the selection pressures which facilitate the evolution of associative learning. The results of these simulations suggest that ecological factors might prevent the selection from operating in favour of those elements of the "learning machinery" which, given the varying nature of the environment, are of potential benefit for the agents. Other simulations highlight the properties of the agent control structure and the characteristics of particular features of the ecology of the learning scenario which facilitate the evolution of learning agents

Graduate

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