Jong Wan Hu

Tunneling projects face several risks during the execution stage that affect the execution objectives (cost, time, quality, and safety). This study aimed to define the main execution activities of machinery tunnels with the associated risk factors and to develop a model for evaluating and analyzing the effects of the risk factors on the execution stage. The recognized activities of executing tunnels included the following: (A01) thrust and reception shaft installation; (A02) machine setup and break-in; (A03) machine progression and lining placing; and (A04) machine break-out and removal. Additionally, thirty-two risk factors associated with these activities were identified. Risk factor probability of occurrence and impacts on cost, time, quality, and safety were determined. Due to this risky and uncertain environment, the fuzzy logic method was applied for developing a model to analyze the effects of the risks on the tunneling process. The model was applied and verified using data collected in Egypt. Many correlations were determined among risk factors that affected tunneling execution objectives, resulting in close relationships with each other. The results emphasized many significant risk factors, such as “conflict between technical geological report and the ground nature”, and “shaft wall damage during break-out”. A03, which is related to machine progression and lining placing, was declared the riskiest activity group during tunneling execution. Further, safety was rated as the objective most affected by risks. The risk model presented in this study can be modified and applied to other cases, while the results and key risks can support the decision-makers who deal with tunneling construction.

Stress has an impact, not only on a person’s physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.