The Finite-Difference Time-Domain Method (FDTD) is a very powerful numerical method for the full wave analysis electromagnetic phenomena. Due to its flexibility, it can be used to solve numerous electromagnetic scattering problems on microwave circuits, dielectrics, and electromagnetic absorption in biological tissue at microwave frequencies. However, it needs so much computation time to simulate microwave integral circuits by applying the FDTD method. If the structure we simulated is complicated and we want to obtain accurate frequency domain scattering parameters, the simulation time will be so much longer that the efficiency of simulation will be bad as well. Therefore, in the thesis, we introduce an artificial neural networks (ANN) method called ¡§Infinite Impulse Response Neural Networks (IIRNN)¡¨ can speed up the FDTD simulation time. In order to boost the efficiency of the FDTD simulation time by stopping the simulation after a sufficient number of time steps and using FIRNN as a predictor to predict time series signal.

The enterprise finance crisis forecast could provide alarm to managers and investors of the enterprise, many scholars advised different alarm models to explain and predict the enterprise is facing finance crisis or not. These models can be classified into three categories by analysis method, the first is single-variate model, it¡¦s easy to implement. The second is multi-variate model which need to fit some statistical assumption, and the third is Artificial Neural Network model which doesn¡¦t need to fit any statistical assumption. However, these models do not consider the industrial effect, different industry could have different finance crisis pattern. This study uses the advantage of Artificial Neural Network to build the process of the enterprise finance crisis forecast model, because it doesn¡¦t need to fit any statistical assumption. Finally, the study use reality finance data to prove the process, and compare with the other models. The result shows the model issued by this study is suitable in Taiwan Electronic Industry, but the performance in Taiwan architecture industry is not better than other models.

Induction motor is the most popular machine in the industry. It is used extensively in mechanical plants, and it is un- avoidable to have the motor¡¦s electrical and mechanical faults due to continuously operating throughout the year. Faults of motors do not only cause the production line to shut down but also imperil the personnel security. A suitable motor maintenance schedule will be a needed to decrease the machine down time. However, major investment might take up to 90% for equipment, and it would be helpful to have a practicable low-cost supervisory scheme on maintenance. If the faults of machine can be detected correctly and effectively, the maintenance efficiency and dependability could be increased greatly. In the past, researches on fault recognition for Induction motors only concentrated on Spectrum analysis with amplitudes based on a constant load. However, the frequency and amplitude of the spectrum analyzed under different fault conditions are also affected significantly by load variations. Using spectrum amplitudes to recognize motor faults is not sufficient in a practical system. Various types of faults and load conditions will influence the spectrum structure. In order to recognize faults under various load conditions, we must consider band shift and amplitude variation as two major factors. In this paper, we use the methods of frequency axis adjustment, load interval and feature exaction to solve the band shift and amplitude variation problems respectively. After the above-mentioned procedures, efficient features are obtained. We use the Back Propagation Neural Network (BPNN) and General Regression Neural Network (GRNN) to train and recognize fault conditions.

In recent years, artificial neural networks (ANNs) have been employed for estimation and prediction purposes in many areas of civil/structural engineering. In this thesis, multilayered feedforward backpropagation algorithm is used for the approximate analysis and calibration of RC T-beam bridges and modeling of bridge ratings of these bridges. Currently bridges are analyzed using a standard FEM program. However, when a large population of bridges is concerned, such as the one considered in this project (Pennsylvania T-beam bridge population), it is impractical to carry out FEM analysis of all bridges in the population due to the fact that development and analysis of every single bridge requires considerable time as well as effort. Rapid and acceptably approximate analysis of bridges seems to be possible using ANN approach. First part of the study describes the application of neural network (NN) systems in developing the relationships between bridge parameters and bridge responses. The NN models are trained using some training data that are obtainedfrom finite-element analyses and that contain bridge parameters as inputs and critical responses as outputs. In the second part, ANN systems are used for the calibration of the finite element model of a typical RC T-beam bridge -the Manoa Road Bridge from the Pennsylvania&rsquo / s T-beam bridge population - based on field test data. Manual calibration of these models are extremely time consuming and laborious. Therefore, a neural network- based method is developed for easy and practical calibration of these models. The ANN model is trained using some training data that are obtained from finite-element analyses and that contain modal and displacement parameters as inputs and structural parameters as outputs. After the training is completed, fieldmeasured data set is fed into the trained ANN model. Then, FE model is updated with the predicted structural parameters from the ANN model. In the final part, Neural Networks (NNs) are used to model the bridge ratings of RC T-beam bridges based on bridge parameters. Bridge load ratings are calculated more accurately by taking into account the actual geometry and detailing of the T-beam bridges. Then, ANN solution is developed to easily compute bridge load ratings.

Estimation of construction durations is a very crucial part of project planning, as several key decisions are based on the estimated durations. In general, construction durations are estimated by using planning and scheduling techniques such as Gannt or bar chart, the Critical Path Method (CPM), and the Program Evaluation and Review Technique (PERT). However, these techniques usually require detailed design information for estimation of activity durations and determination of the sequencing of the activities. In some cases, pre-design duration estimates may be performed by using these techniques, however, accuracy of these estimates mainly depends on the experience of the planning engineer. In this study, it is aimed to develop and compare alternative methods for conceptual duration estimation of building constructions with basic data information available at the early stages of projects. Five parametric duration estimation models are developed with the data of 17 building projects which were constructed by a contractor in United States. Regression analysis and artificial neural networks are used in the development of these five duration estimation models. A parametric cost estimation model is developed using regression analysis for cost estimations to be used in calculating the prediction performances of cost based duration estimation models. Finally, prediction performances of all parametric duration estimation models are determined and compared. The models provided reasonably accurate estimates for construction durations. The results also indicated that construction durations can be predicted accurately without making an estimate for the project cost.

In recent years, Artificial Neural Networks (ANN) have become widely popular tools in various disciplines of engineering, including civil engineering. In this thesis, Multi-layer perceptron with back-propagation type of network is utilized in calibration of the finite element model of a long span cantilever through truss called Commodore Barry Bridge (CBB). The essence of calibration lies in the phenomena of comparing and correlating the structural response of an analytical model with experimental results as closely as possible. Since CBB is a very large structure having complex structural mechanisms, formulation of mathematical expressions representing the relation between dynamics of the structure and the structural parameters is very complicated. Furthermore, when the errors in the structural model and noise in the experimental data are taken into account, a calibration study becomes more tedious. At this point, ANNs are useful tools since they have the capability of learning with noisy data and ability to approximate functions. In this study, firstly sensitivity analyses are conducted such that variations in dynamic properties of the bridge are observed with the changes in its structural parameters. In the second part, inverse relation between sensitive structural parameters and modal frequencies of CBB is approximated by training of a neural network. This successfully trained network is then fed up with experimental frequencies to acquire the as-is structural parameters and model updating is achieved accordingly.

This thesis analyzes the relationship between users&rsquo / characteristics and users&rsquo / interface preferences. An online survey is developed for this study. This survey composed of two types of questions: (1) users&rsquo / personal information such as age, gender, country, cognitive structure, and also computer experience and (2) user interface elements. More than 2,500 participants from 120 different countries throughout the world completed our survey. Results were analyzed using cross tables. Our findings show that there is a relationship between users&rsquo / characteristics and users&rsquo / interface preferences. In the presence of this relationship, an artificial neural network model is developed for the estimation of the interface preferences based on the user characteristics.

Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo / Matlab R 2007b&rdquo / software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

Apatite group of minerals have been widely used in applications like detoxification of wastes, disposal of nuclear wastes and energy applications in addition to biomedical applications like bone repair, substitution, and coatings for metal implants due to its resemblance to the mineral part of the bone and teeth. X-ray diffraction patterns of bone are similar to mineral apatites such as hydroxyapatite and fluorapatite. Formation and physicochemical properties of apatites can be understood better by computer modeling. For this reason, lattice parameters of possible apatite compounds (A10(BO4)6C2), constituted by A: Na+, Ca2+, Ba2+, Cd2+, Pb2+, Sr2+, Mn2+, Zn2+, Eu2+, Nd3+, La3+, Y3+ / B: As+5, Cr+5, P5+, V5+, Si+4 / and C: F-, Cl-, OH-, Br-1 were predicted from their elemental ionic radii by artificial neural networks techniques. Using artificial neural network techniques, prediction models of lattice parameters a, c and hexagonal lattice volumes were developed. Various learning methods, neuron numbers and activation functions were used to predict lattice parameters of apatites. Best results were obtained with Bayesian regularization method with four neurons in the hidden layer with &lsquo / tansig&rsquo / activation function and one neuron in the output layer with &lsquo / purelin&rsquo / function. Accuracy of prediction was higher than 98% for the training dataset and average errors for outputs were less than 1% for dataset with multiple substitutions and different ionic charges at each site. Non-stoichiometric apatites were predicted with decreased accuracy. Formulas were derived by using ionic radii of apatites for lattice parameters a and c.

The main objective of this work is to develop models using multivariable regression and artificial neural network approaches for cost estimation of the construction costs of trackworks of light rail transit and metro projects at the early stages of the construction process in Turkey. These two approaches were applied to a data set of 16 projects by using seventeen parameters available at the early design phase. According to the results of each method, regression analysis estimated the cost of testing samples with an error of 2.32%. On the other hand, artificial neural network estimated the cost with 5.76% error, which is slightly higher than the regression error. As a result, two successful cost estimation models have been developed within the scope of this study. These models can be beneficial while taking the decision in the tender phase of projects that includes trackworks.