Structural Damage Prediction Under Seismic Sequence Using Neural Networks

Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been already conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble neural network approach is applied to predict the final structural damage of an 8-storey reinforced concrete frame under real and artificial ground motion sequences. Successive earthquakes consisted of two seismic events are utilised. We considered 16 well-known ground motion intensity measures and the structural damage that occurred by the first earthquake as the features of the machine-learning problem, while the final structural damage was the target. After the first seismic events and after the seismic sequences, both actual values of damage indices are calculated through nonlinear time history analysis. The machine-learning model is trained using the dataset generated from artificial sequences. Finally, the predictive capacity of the fitted neural network is accessed using the natural seismic sequences as a test set

Модернизация конструкции разрезного трубного гидравлического ключа FARR KT5500

Целью работы является модернизация конструкции разрезного трубного гидравлического ключа FARR KT5500. Дипломный проект содержит аналитическую часть, проектную часть – реверс-инжиниринг гидравлического ключа - модернизация роторного узла, раздел по обеспечению безопасности труда, анализ финансовой эффективности проекта, список использованных источников. По результатам исследования предложена модернизация роторного узла разрезного трубного гидравлического ключа FARR KT5500, позволяющая оптимизировать процесса свинчивания – развинчивания обсадных и бурильных труб при проведении буровых работ.The aim of the work is to modernize the design of the FARR KT5500 split pipe hydraulic tong. The diploma project contains an analytical part, the design part - reverse engineering of a hydraulic tong - modernization of the rotor unit, a section on labor safety, an analysis of the financial efficiency of the project, a list of sources used. Based on the results of the study, it was proposed to modernize the rotary unit of the FARR KT5500 split pipe hydraulic tong, which allows to optimize the make-up process - unscrewing of casing and drill pipes during drilling operations

Hilbert-Huang Transform-Based Seismic Intensity Parameters for Performance-Based Design of RC-Framed Structures

This study aims to develop the optimal artificial neural networks (ANNs) capable of estimating the seismic damage of reinforced concrete (RC)-framed structures by considering several seismic intensity parameters based on the Hilbert–Huang Transform (HHT) analysis. The selected architecture of ANN is the multi-layer feedforward perceptron (MFP) network. The values of the HHT-based parameters were calculated for a set of seismic excitations, and a combination of five to twenty parameters was performed to develop input datasets. The output data were the structural damage expressed by the Park and Ang overall damage index (DIPA,global). The potential contribution of nine training algorithms to developing the most effective MFP was also investigated. The results confirm that the evolved MFP networks, utilizing the employed parameters, provide an accurate estimation of the target output of DIPA,global. As a result, the developed MFPs can constitute a reliable computational intelligence approach for determining the seismic damage induced on structures and, thus, a powerful tool for the scientific community for the performance-based design of buildings