Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well.

Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.

Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.

Filtration is an important process in drinking water treatment to ensure the adequate removal of particle-bound pathogens (i.e. Giardia and Cryptosporidium). Filtration performance is typically monitored in terms of filtered water turbidity. However, particle counts may provide further insight into treatment efficiency, as they have a greater sensitivity for detecting small changes in filtration operation. To optimize the filtration process at the Elgin Area WTP in terms of post-filtration particle counts, artificial neural network (ANN) models were applied. Process models were successfully developed to predict settled water turbidity and particle counts. Additionally, two inverse process models were developed to predict the optimal coagulant dosage required to attain target particle counts. Upon testing each model, a high correlation was observed between the actual and predicted data sets. The ANNs were then integrated into an optimization application to allow for the transfer of real-time data between the models and the SCADA system.

Filtration is an important process in drinking water treatment to ensure the adequate removal of particle-bound pathogens (i.e. Giardia and Cryptosporidium). Filtration performance is typically monitored in terms of filtered water turbidity. However, particle counts may provide further insight into treatment efficiency, as they have a greater sensitivity for detecting small changes in filtration operation. To optimize the filtration process at the Elgin Area WTP in terms of post-filtration particle counts, artificial neural network (ANN) models were applied. Process models were successfully developed to predict settled water turbidity and particle counts. Additionally, two inverse process models were developed to predict the optimal coagulant dosage required to attain target particle counts. Upon testing each model, a high correlation was observed between the actual and predicted data sets. The ANNs were then integrated into an optimization application to allow for the transfer of real-time data between the models and the SCADA system.

In this study, an alternative seismic vulnerability assessment model is developed. For this purpose, one of the most popular artificial intelligence techniques, Artificial Neural Network (ANN), is used. Many ANN models are generated using 4 different network training functions, 1 to 50 hidden neurons and combination of structural parameters like number of stories, normalized redundancy scores, overhang ratios, soft story indices, normalized total column areas, normalized total wall areas are used to achieve the best assessment performance. Duzce database is used throughout the thesis for training ANN. A neural network simulator is developed in Microsoft Excel using the weights and parameters obtained from the best model created at Duzce damage database studies. Afyon, Erzincan, and Ceyhan databases are simulated using the developed simulator. A recently created database named Zeytinburnu is used for the projection purposes. The building sesimic vulnerability assessment of Zeytinburnu area is conducted on 3043 buildings using the proposed procedure.

In recent years, artificial neural networks (ANNs) have been applied to estimate in many areas of hydrology and hydraulic engineering. In this thesis, multilayered feedforward backpropagation algorithm was used to establish for the prediction of unit discharge q (m3/s/m) in a rectangular free overfall. Researchers&rsquo / experimental data were used to train and validate the network with high reliability. First, an appropriate ANN model has been established by considering determination of hidden layer and node numbers related to training function and training epoch number. Then by applying sensitivity analysis, parameters involved in and their effectiveness relatively has been determined in the phenomenon. In the scope of the thesis, there are two case studies. In the first case study, ANN models reliability has been investigated according to the training data clustered and the results are given by comparing to regression analysis. In the second case, ANN models&rsquo / ability in establishing relations with different data clusters is investigated and effectiveness of ANN is scrutinized.

Damage to steel moment frames in the 1994 Northridge and 1995 Hyogken-Nanbu earthquakes subsequently motivated intensive research and testing efforts in the US, Japan, and elsewhere on moment frames. Despite extensive past research efforts, one important problem remains unresolved: the degree of panel zone participation that should be permitted in the inelastic seismic response of a steel moment frame. To date, a fundamental computational model has yet to be developed to assess the cyclic rupture performance of moment frames. Without such a model, the aforementioned problem can never be resolved. This dissertation develops an innovative way of predicting cyclic rupture in steel moment frames by employing artificial neural networks. First, finite element analyses of 30 notched round bar models are conducted, and the analytical results in the vicinity of the notch root are extracted to form the inputs for either a single neural network or a competitive neural array. After training the neural networks, the element with the highest potential to initiate a fatigue crack is identified, and the time elapsed up to the crack initiation is predicted and compared with its true synthetic answer. Following similar procedures, a competitive neural array comprising dynamic neural networks is established. Two types of steel-like materials are created so that material identification information can be added to the input vectors for neural networks. The time elapsed by the end of every stage in the fracture progression is evaluated based on the synthetic allocation of the total initiation life assigned to each model. Then, experimental results of eight beam-to-column moment joint specimens tested by four different programs are collected. The history of local field variables in the vicinity of the beam flange - column flange weld is extracted from hierarchical finite element models. Using the dynamic competitive neural array that has been established and trained, the time elapsed to initiate a low cycle fatigue crack is predicted and compared with lab observations. Finally, finite element analyses of newly designed specimens are performed, the strength of their panel zone is identified, and the fatigue performance of the specimens with a weak panel zone is predicted.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

<p>n this dissertation a comparison of two evolutionary methods for evolving ANNs for robot control is made. The methods compared are SANE with enforced sub-population and delta-coding, and marker-based encoding. In an attempt to speed up evolution, marker-based encoding is extended with delta-coding. The task selected for comparison is the hunter-prey task. This task requires the robot controller to posess some form of memory as the prey can move out of sensor range. Incremental evolution is used to evolve the complex behaviour that is required to successfully handle this task. The comparison is based on computational power needed for evolution, and complexity, robustness, and generalisation of the resulting ANNs. The results show that marker-based encoding is the most efficient method tested and does not need delta-coding to increase the speed of evolution process. Additionally the results indicate that delta-coding does not increase the speed of evolution with marker-based encoding.</p>