Modelling and simulation
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AbstractThis paper is part of a research theme to develop methods that enhance risk assessment studies by the use of 'automated' failure analysis. The paper presents an approach to mechanical failure analysis and introduces a mechanical failure analysis module that can be used in a virtual reality (VR) environment. The module is used to analyse and predict failures in mechanical assemblies; it considers stress related failures within components, as well as failures due to component interactions. Mechanical failures are divided into two categories in this paper: material failures and interference failures. The former occur in components and the latter happen at the interface between components. Individual component failures can be analysed readily; a contribution of the mechanical failure analysis module is to predict interference failures. A mechanical failure analysis system that analyses and visualizes mechanical failures in a virtual environment has been developed. Two case studies demonstrate how the system carries out failure analysis and visualization as design parameters are changed.
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CitationLi J-P and Thompson GP (2005) Mechanical failure analysis in a virtual reality environment. Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering. 219(3): 237-250.
Link to publisher’s version10.1243/095440805X28258
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An approach to failure prediction in a cloud based environmentAdamu, Hussaini; Bashir, Mohammed; Bukar, Ali M.; Cullen, Andrea J.; Awan, Irfan U. (2017)Failure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers’ data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime.
Tests of continuous concrete slabs reinforced with basalt fibre reinforced plastic barsKara, Ilker F.; Köroğlu, Mehmet A.; Ashour, Ashraf F. (2017)This paper presents experimental results of three continuously supported concrete slabs reinforced with basalt-fibre-reinforced polymer (BFRP) bars. Three different BFRP reinforcement combinations of over and under reinforcement ratios were applied at the top and bottom layers of continuous concrete slabs tested. One additional concrete continuous slab reinforced with steel bars and two simply supported slabs reinforced with under and over BFRP reinforcements were also tested for comparison purposes. All slabs sections tested had the same width and depth but different amounts of BFRP reinforcement. The experimental results were used to validate the existing design guidance for the predictions of moment and shear capacities, and deflections of continuous concrete elements reinforced with BFRP bars. The continuously supported BFRP reinforced concrete slabs illustrated wider cracks and larger deflections than the control steel reinforced concrete slab. All continuous BFRP reinforced concrete slabs exhibited a combined shear–flexure failure mode. ACI 440-1R-15 equations give reasonable predictions for the deflections of continuous slabs (after first cracking) but stiffer behaviour for the simply supported slabs, whereas CNR DT203 reasonably predicted the deflections of all BFRP slabs tested. On the other hand, ISIS-M03-07 provided the most accurate shear capacity prediction for continuously supported BFRP reinforced concrete slabs among the current shear design equations.
Experimental response and code modelling of continuous concrete slabs reinforced with BFRP barsMahroug, Mohamed E.M.; Ashour, Ashraf F.; Lam, Dennis (2014)This paper presents test results and code predictions of four continuously and two simply supported concrete slabs reinforced with basalt fibre reinforced polymer (BFRP) bars. One continuously supported steel reinforced concrete slab was also tested for comparison purposes. All slabs tested were 500 mm in width and 150 mm in depth. The simply supported slabs had a span of 2000 mm, whereas the continuous slabs had two equal spans, each of 2000 mm. Different combinations of under and over BFRP reinforcement at the top and bottom layers of slabs were investigated. The continuously supported BFRP reinforced concrete slabs exhibited larger deflections and wider cracks than the counterpart reinforced with steel. Furthermore, the over reinforced BFRP reinforced concrete slab at the top and bottom layers showed the highest load capacity and the least deflection of all BFRP slabs tested. All continuous BFRP reinforced concrete slabs failed owing to combined shear and flexure at the middle support region. ISIS-M03-07 and CSA S806-06 design guidelines reasonably predicted the deflection of the BFRP slabs tested. However, ACI 440-1R-06 underestimated the BFRP slab deflections and overestimated the moment capacities at mid-span and over support sections.