Joint Kinematics and Sealing Capacity Assessment of Ductile Iron Pipes under Abrupt Transverse Ground Movements

Joint kinematic behaviour, i.e., joint rotation and axial translation, can partially help pipelines to accommodate abrupt ground movements, and cause leaking if joint service limit is exceeded, even without any structural failure. Kinematic behaviour of bell-spigot jointed ductile iron (DI) pipes and its influence on joint sealing capacity under abrupt transverse ground movements are investigated in this study. Firstly, a beam-on-spring finite element analysis on joint kinematics of DI pipes is conducted, in which different fault-pipe crossing positions are implemented. Based on simulated results, a modified joint kinematic solution incorporating pipe deflection and joint shear force under different fault-pipe crossing positions is proposed. Then, a Monte Carlo simulation (MCS)-based reliability assessment procedure for joint sealing capacity is developed. Sensitivity analysis is subsequently conducted to investigate the effects of uncertainties associated with initial axial translation, soil properties, and crossing positions on the joint sealing capacity, and the effects of different deterministic solutions are compared. The proposed method allows engineers to effectively evaluate how the joint sealing capacity of DI pipes changes with consideration of uncertainties when abrupt transverse ground movements are encountered.

Classifying Upper Arm Gym-Workouts via Convolutional Neural Network by Imputing a Biopotential-Kinematic Relationship

This paper proposes a systemic approach to upper arm gym-workout classification according to spatio-temporal features depicted by biopotential as well as joint kinematics. The key idea of the proposed approach is to impute a biopotential-kinematic relationship by merging the joint kinematic data into a multichannel electromyography signal and visualizing the merged biopotential-kinematic data as an image. Under this approach, the biopotential-kinematic relationship can be imputed by counting on the functionality of a convolutional neural network: an automatic feature extractor followed by a classifier. First, while a professional trainer is demonstrating upper arm gym-workouts, electromyography and joint kinematic data are measured by an armband-type surface electromyography (sEMG) sensor and a RGB-d camera, respectively. Next, the measured data are augmented by adopting the amplitude adjusted Fourier Transform. Then, the augmented electromyography and joint kinematic data are visualized as one image by merging and calculating pixel components in three different ways. Lastly, for each visualized image type, upper arm gym-workout classification is performed via the convolutional neural network. To analyze classification accuracy, two-way rANOVA is performed with two factors: the level of data augmentation and visualized image type. The classification result substantiates that a biopotential-kinematic relationship can be successfully imputed by merging joint kinematic data in-between biceps- and triceps-electromyography channels and visualizing as a time-series heatmap image.