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ISRA: Improver societal resilience analysis for critical infrastructure
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
H. Rosenqvist, N.K. Reitan, L. Petersen, D. Lange
The assessment is performed by qualitatively scoring a set of indicators on a scale from 1 to 5. The indicators are categorized according to the six resilience dimensions at Level 2 of the ISRA structure (Fig. 2). The score for each resilience dimension is achieved by aggregating the indicators under each dimension and capacity to a single measurement, and thereafter aggregating the three capacity scores into one score for each resilience dimension. In this early development phase of ISRA, all indicators and subcategories are weighted equal, but one might want to weigh the importance of the capacities and dimensions in order to capture subjective resilience aspects. The indicators are aggregated by the weighted arithmetic mean (Eq. 1) () Levelkindicator=∑i=1nwixi
Aggregation of uncertain information and its implementation in geographic information systems and spatial databases
Published in Soňa Molčíková, Viera Hurčíková, Vladislava Zelizňaková, Peter Blišťan, Advances and Trends in Geodesy, Cartography and Geoinformatics, 2018
R. Ďuračiová, M. Muňko, J. Caha
Note, that he last two cases correspond to the Zadeh’s conventional fuzzy logical operators. The weighting of individual criteria is often used in the aggregation process. Therefore, the weighted arithmetic mean (average) hw is defined as follows: hw(cx1,…an)=∑i=1nwicxl, $$ h_{w} (cx_{1} , \ldots a_{n} ) = \mathop \sum \limits_{{i = 1}}^{n} w_{i} cx_{l} , $$
Data normalisation — Levelling the playing field
Published in Alan R. Jones, Principles, Process and Practice of Professional Number Juggling, 2018
We can calculate the Equivalent ‘Outturn’ Budget required at ‘Then Year’ Economic Conditions (ECs) by factoring up the ‘Base Year’ Values for escalation. This is equivalent to multiplying the Total Budget at ‘Base Year’ ECs by a Weighted Arithmetic Mean of the Escalation Factors, where: The Escalation Factors are the ‘Then Year’ Escalation Indices divided by ‘Base Year’ Escalation IndexThe weightings are the annual percentages of the ‘Base Year’ Budget Conversely in Table 6.23, we can ‘reverse engineer’ an Equivalent ‘Outturn’ Budget back to ‘Base Year’ Values by factoring down from the ‘Then Year’ Values. This is equivalent to dividing the Total Budget at ‘Then Year’ ECs by a Weighted Harmonic Mean of the Escalation Factors (see Volume II Chapter 2), where: The Escalation Factors are the ‘Then Year’ Escalation Indices divided by ‘Base Year’ Escalation IndexThe weightings are the annual percentages of the ‘Then Year’ Budget When we are dealing with ‘Base Year’ or any ‘Constant Year’ values, life is more straightforward. Any shift to the right or left in the programme overall over a fixed duration will not affect our budget profile, provided the ‘Base Year’ Economic Conditions do not alter. However, if we are dealing with ‘Then Year’ values, any shift to the right or left in the programme overall will fundamentally change the Equivalent ‘Outturn’ Budget due to the change in the weightings and Escalation Factors, as illustrated in Table 6.24 and Figure 6.10.
Condition and criticality-based predictive maintenance prioritisation for networks of bridges
Published in Structure and Infrastructure Engineering, 2022
Georgios M. Hadjidemetriou, Manuel Herrera, Ajith K. Parlikad
The proposed methodology estimates bridge criticality not only based on the ABA-closeness vitality ranking (R1), but also on the observed traffic passing by a bridge. Thus, there is a second bridge ranking (R2), with the bridge with highest traffic positioned first. A weighted arithmetic mean of the two rankings estimates the final ranking, as follows: where MPI stands for Maintenance Prioratisation Index. A weighted arithmetic mean is calculated by data points which do not equally contribute to the final average. In case of all weights being equal (i.e. w1=w2=0.5), then the weighted arithmetic mean is the arithmetic mean.