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Risk-based evaluation of main suspension cables of the Forth Road Bridge in Scotland
Published in Khaled M. Mahmoud, Risk-based Bridge Engineering, 2019
K. Mahmoud, C. Gair, H. McDonald
Due to the fact that not every wire is actually sampled, there would be a sampling error. It describes the range that the estimated cable strength is likely to fall within. In visual-based selection of wire samples, there is an obvious visual bias. Further, the sampling error, which is the degree to which the sample differs from the population, remains unknown. In random sampling, it is possible to quantify the sampling error and thus provide more reliable analysis of results. Each wire in the available pool for sampling has an equal and known chance of being selected. Because random sampling is a fair way to select a wire sample, it is reasonable to infer the strength of the entire cable from the test results produced by the randomly selected wire sample. In fact, random sampling procedures do increase the probability that the randomly selected wires will be representative of the cable condition. In the design of the sampling plan, it is important to realize that sampling should be limited to provide an acceptable level of error in the estimated cable strength. It is not feasible to sample wires too deep in the wedge opening, because clearance issues would impede splicing and tensioning of the replacement wire. It is important to replace every single wire sample with a spliced new wire in its place to avoid the presence of a void between the wires. Such a void could house moisture, which is of damaging consequences to the high strength steel wire.
Sampling strategies
Published in Andrew Metcalfe, David Green, Tony Greenfield, Mahayaudin Mansor, Andrew Smith, Jonathan Tuke, Statistics in Engineering, 2019
Andrew Metcalfe, David Green, Tony Greenfield, Mahayaudin Mansor, Andrew Smith, Jonathan Tuke
The sampling error can be estimated and quantified by its standard error. A strategy for estimating the errors in specification and cost of schemes is given in the next section. The chance of missing schemes can be reduced by making a thorough survey5. It is better to survey a few zones carefully than it is to take large samples and survey them in a cursory manner. Changes in engineering procedures, such as may arise from improvements in no-dig tunneling equipment, could lead to reduced costs. Inflation is more of an accounting issue, and an AMP is typically presented in terms of prices at some fixed date.
How Have Causes Been Identified?
Published in Susan B. Norton, Susan M. Cormier, Glenn W. Suter, Ecological Causal Assessment, 2014
Glenn W. Suter, Susan M. Cormier
In field studies, statistical hypothesis tests can be misleading for several reasons. Assumptions of tests usually are not met, as treatments are not replicated or randomly applied (e.g., sewage outfalls are not randomly placed on different streams). Very large sample sizes can find statistical significance in a small, biologically meaningless difference. Small sample sizes or high sampling error may cause a biologically relevant difference to not be statistically significant. An illustrative example is provided in Box 3.1.
Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics
Published in International Journal of Production Research, 2023
Kuen-Suan Chen, Yuan-Lung Lai, Ming-Chieh Huang, Tsang-Chuan Chang
The samples obtained using statistical sampling are in fact only a small portion of the population. For this reason, a difference exists between the and derived from the observed values of the samples and the true population parameters; this difference is called the sampling error. To reduce sampling error, many researchers use large samples to increase the accuracy of their inferences. However, this approach still does not eliminate sampling error. Interval estimation provides a possible range for estimation of the population parameters, and this range captures the true population parameters with a certain degree of confidence (i.e. level of confidence). Therefore, interval estimation can provide more information about population parameters than point estimation can.
A taxonomy of digital leadership in the construction industry
Published in Construction Management and Economics, 2021
Sambo Lyson Zulu, Farzad Khosrowshahi
We note the methodological limitations of the study. First, the selected study sample may introduce a sampling error that the sample may not be representative of the population. Based on the recommendations in the literature, the sample characteristics were inspected to ensure that the participants fitted the sample inclusion criteria. The sample demography data demonstrated that the participants fulfilled the inclusion criteria and, therefore, answered the questionnaire based on their experience. Second, we acknowledge that the construction industry is represented by various roles and organization types, and therefore, the participants in this study do not represent the whole spectrum of the construction industry. Readers should therefore interpret the findings with this limitation in mind. Future studies can build upon our study and evaluate the extent to which these typologies apply in the different organizational settings of the construction industry.
Fuzzy assessment model to judge quality level of machining processes involving bilateral tolerance using crisp data
Published in Journal of the Chinese Institute of Engineers, 2021
Chien-Che Huang, Tsang-Chuan Chang, Bae-Ling Chen
As mentioned in Section 2, the MLE of can be employed to analyze whether or not process quality level achieves customer requirements. Unfortunately, as is based on samples collected at random from a stable process, sampling error cannot be eliminated entirely. Therefore, it is necessary to qualify the estimate using confidence intervals (CIs) (Zimmer 2000); indeed, application of confidence intervals to indices in the assessment of process performance has become standard (Besseris 2019; Liao et al. 2017; Kashif et al. 2017; Kanichukattu and Luke 2013; Wu and Huang 2010). In view of this, this study attempts to establish the CI of .