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Across the world, the acceptance criterion for concrete made with non-potable water (SA) is stipulated for mean strength with reference to similar concrete made with potable water (SP). The basis for the strength design of concrete is the characteristic strength. The effect of the acceptance criteria on the characteristic strength of SA and SP was examined using statistical simulations, for the first time. It was found that, compared with a two-sample set, using a three-sample set would improve the characteristic strength of SA. A higher allowable ratio between the means of SA and SP and a lower deviation of individual samples from the mean would improve the characteristic strength of SA. The effect of the coefficient of variation (CoV) of the original population on the characteristic strength of SA varied. On the one hand, a higher CoV reduced the characteristic strength of SP, thereby increasing the probability of SA strength being higher than SP strength. On the other, a higher CoV of SP meant a wider spread of the samples and that could increase the CoV of SA, thereby reducing the characteristic strength of SA. Probabilistic charts were developed for the characteristic strengths of SA and SP for different combinations of the aforementioned variables, with guidance for practical application. [ABSTRACT FROM AUTHOR]

Extreme rainfall is an important consideration for safety of nuclear installations. Literature reported estimation of the same for Indian nuclear power plant sites using Gumbel and Frechet distributions, fitted by least square (graphical) method (LSM). In these reports, the goodness-of-fit was examined graphically by plots on probability papers. In this study, the data for six nuclear sites collected from literature were analysed. For this purpose, in addition to Gumbel and Frechet, other distributions were included and the parameters of the distributions were evaluated using Lieblein technique and maximum likelihood estimation, as well as LSM. The goodness-of-fit was evaluated using the quantitative tool: Anderson-Darling test; and qualitative tools: probability plot and quantile plot – which are suitable for evaluating goodness-of-fit of extreme value distributions. Maximum daily rainfall for return periods of 50, 100 and 1000 years were reported from the fitted distributions. Comparison with the respective values (50 and 100 year return period) from literature demonstrated that the extreme rainfall reported for these sites were quite accurate with maximum variation within ten per cent.

India experiences rainfall from southwest and northeast monsoon, with large spatial and temporal variability reported in recent years. Rainfall trend could be essential for disaster preparedness or long-term planning of agriculture and economic advancement. Long-term and short-term trends observed in rainfall across different subdivisions of India could be different. The result of trend (or any other) analysis could vary when different tests are employed and hence it is advisable to employ more than one test for any statistical check. Therefore, four tests were employed for detection of trend including Mann-Kendall test, Spearman's Rank Order Correlation test, Wald-Wolfowitz Run test on data, and Wald-Wolfowitz Run test on successive difference of data; and the slopes were estimated by Sen's slope as well as linear regression analysis. For detection of possible change point, the four tests included Pettitt's test, Von Neumann Ratio test, Buishand's Range test, and the graphical test of Cumulative Departures from Mean. Cases of conflicting results in the four tests were addressed with categorical inferences: useful (one or less test rejects null hypothesis); doubtful (two tests reject null hypothesis); suspect (three or more test rejects null hypothesis) – based on approach described in literature. For seven subdivisions in India, trend was statistically significant at 5% level, out of which for five subdivisions, change point was also identified. Decreasing trend was observed for: Nagaland, Manipur, Mizoram, and Tripura; East Uttar Pradesh; East Madhya Pradesh; and Chhattisgarh, whereas increasing trends were identified for Konkan and Goa; Coastal Karnataka; and Telangana. Change point was detected for four other subdivisions as well. The mean was observed to be unstable for nineteen subdivisions, which included all nine subdivisions with identified change points. The short-term trend was found to be at variance with the long term trend in several subdivisions and significant trend was observed for Saurashtra, Kutch and Diu in the last quarter period (1980–2016). Such findings highlight the necessity of short-term trend analysis for indications of possible climate change effects on recent rainfall records. [ABSTRACT FROM AUTHOR]

Increasing trend of number and intensity of the extreme precipitation events in India has been reported in literature. Majority of the Indian weather stations have historical rainfall data recorded at a fixed time (0830 hours) of the day, which could be a limitation for correct estimation of extremes. To overcome this, clock hour correction factor (CHCF) in extreme value analysis (EVA) assumes importance. With hourly rainfall data from a site on western coast of India, CHCF was estimated and compared with those reported in literature. The CHCF estimated in this study was higher than those suggested for India in literature or codes. Further, the effect of the CHCF on EVA was examined for India taking CHCF from literature and Indian standards. Towards this, EVA was performed with fixed window (FW) maxima (with CHCF) and sliding window (SW) maxima obtained from the hourly rainfall records of the site. The extreme rainfall from EVA with FW monthly maxima (68 entries) along with CHCF was conservative, compared to those with SW monthly maxima. Annual FW data (17 entries) resulted in unacceptable extreme values (with or without CHCF). This could be due to the insufficient data length and different distribution shape for annual FW data. Treating each monsoon month as a block in Block Maxima approach or adopting suitable threshold in Peak-Over-Threshold approach could help to increase data length for EVA from shorter record length of rainfall data. [ABSTRACT FROM AUTHOR]