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.

Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.

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