Srinivas Sriramula

Previous studies have investigated the behaviour of prestressed stayed steel columns with a view to developing design guidelines. As most of the modern standards move towards calibrated safety factors, there is a need to explore the design expectations of these structures from a probabilistic perspective. This has not been studied in the literature till now, perhaps due to lack of clarity on underlying uncertainties, failure models and reliability levels. In this paper, the relevant reliability levels were studied to highlight the critical modes for such structures. A sensitivity analysis was performed on the random variables to investigate their impact on the model output. The reliability levels found through appropriate analysis were then compared with target reliabilities to calibrate partial safety factors to be used in the design of these structures. A range of safety factor values were found depending on the conditions adopted in the calibration and target reliability studies.

Numerous services are available today to develop an optimised asset management solution to enhance asset operations by improving the system availability, decreasing down-time and operation and maintenance costs. Three cases of engineering problems are explored in this paper, with data-driven machine learning solutions proposed for these problems. The first case refers to the labour-intensive nature of criticality analysis which are used in asset management to prioritise assets. A machine learning solution is proposed by the development of a trained criticality analysis model, with a classification error of 12.35%, which could help in a better prediction of the end result by automating the process i.e. training the model. The second case looks at an application of machine learning on asset health prediction by analysing failure patterns and parameters for a machine. The model was evaluated with an error loss of 0.0024. The third case looks at an integration of the priorities related to asset maintenance and management through the development of a text classification machine learning service selector (landscape) tool and explores improvising the end-user selection of the services based on their challenges and perceived pain-points related to asset management. The model was evaluated with an accuracy of 84%.