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Measuring stiffness of soils in situ
Published in Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto, Computer Methods and Recent Advances in Geomechanics, 2014
Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto
Objective- C is a general-purpose, object-oriented programming language that adds Smalltalk-style messaging to the C programming language. It is the main programming lanquage used by Apple for the OS X and iOS operating systems and their respective APIs, Cocoa and Cocoa Touch.
Bridge structural safety assessment: a novel solution to uncertainty in the inspection practice
Published in Structure and Infrastructure Engineering, 2023
Francesca Poli, Mattia Francesco Bado, Andrea Verzobio, Daniele Zonta
Let us now consider the inspective objective C: ‘Need for follow-up studies’. In light of the intrinsic nature of a visual inspection itself, whenever (B = No) is assigned, a certain amount of uncertainty still exists. Indeed, through simple observation of the external surface of a structural element, it is sometimes hard to assess the presence of an internal defect/defects positioned on non-inspectable locations (Gerber saddles for example), its evolution and its effect on the carrying capacity. By means of C, the inspector can express certainty (C = No) or uncertainty (C = 3) in her/his evaluation, by requesting follow-up studies. Note that, follow-up studies could include: calculation of the influence of a certain defect on the designed resistance of the bridge (based on bridge design documentation), Non-Destructive Tests (NDT) or even destructive ones.
Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems
Published in International Journal of Production Research, 2023
Mingjie Bi, Ilya Kovalenko, Dawn M. Tilbury, Kira Barton
In this simulated manufacturing system, 50 L-products and 50 S-products are fed alternatively into the system with a pre-generated initial production schedule. Products enter the facility every 30 ticks starting at tick 10. To provide an opportunity for a rescheduling event to occur, the initial production schedule is designed with 50% resource utilisation. Uncertainty in machine operation time and the probability of machine breakdown are added to all machines in the simulated system. The system starts operations with the probability of machine breakdown ranging from 3.3% to 10%. If a machine undergoes a breakdown, a rescheduling process will be triggered. The mean time to repair ranges from 1000-1500 ticks for a broken machine. Note that if the breakdown occurs when the machine is processing a product, the product will be damaged and cannot be recovered. The rescheduling decision-making considers the completion time of as the objective C, and this case study does not have soft constraints, thus and . We conduct two case studies to evaluate the performance of the proposed distributed method.
Quantifying the influence of urban development on runoff in South Africa
Published in Urban Water Journal, 2022
Ione Loots, Jeffrey Colin Smithers, Thomas Rodding Kjeldsen
For Objective c), the flood peaks were assessed in two ways: i) for the AMS and ii) for all flood peak points in the instantaneous flow data above the 1-year recurrence interval threshold, estimated as the median of the AMS (PoT). The trend over time in the data for each catchment was compared using the Mann–Kendall test. In addition, the trends in peak discharge with increasing URBEXT as assessed. The three catchments with longer data sets (A2H027, A2H029 and A2H030) experienced a number of high peaks before 1982. This influenced the results of both sets of analysis, but especially the PoT analysis. Therefore, both sets of analysis were run twice: first with flood peaks before 1982 included, and then with only flood peaks after 1982. The AMS is shown as a percentage of the 1-year recurrence interval flood peak for each catchment in Figure 7, with linear regression curves shown to identify trends. Of significance are the slopes of the AMS linear regression curves for A2H029, at 0.010 when considering the entire data period and 0.023 when considering the data set from 1982; and A2H063 with dataset starting in 1985, at 0.004. The slope of the PoT linear regression curves for A2H027, at −0.013 considering the entire data period and −0.009 for the data set from 1982, are also noteworthy.