Hojas de notificación de situaciones de riesgo y maltrato infantil

The objective of this thesis was to investigate the effect of disparate informa- tion levels on bridge management and safety, and how the levels of information available to the engineer can severely impact the stability of probabilistic as- sessment. Five specific objectives were outlined in Chapter 1, and they were addressed as follows:

1. In Chapter 3, a structural reliability analysis was conducted on three bridge types typical of Ireland and mainland Europe; all of which were assessed for the limit-states of flexure under a probabilistically defined load model. The emphasis on this work was regarding the levels of uncer- tainty in the model parameters of the physical nature of the bridges, and how these affected different bridge types. It was observed that bridges of similar structural material and form are clustered in terms of sensitivity or parametric importance studies. The levels of existing correlations for the parameters across the bridge types, and how their influences on the reli- ability under varying degrees of uncertainty indicates the importance of a calibrated framework for the assessment of bridges at a network level. The network-level calibration is observed to be strongly dependent on the availability and the quality of information of the bridges within the net- work and, consequently, it can be stated that structural reliability analysis refers more to our state of knowledge of the structure than to the actual state of the structure itself. This emphasizes the need for data-sharing for such structures by the managers and owners of bridge networks for the most reasonable and cost-effective interventions to be carried out.

2-3. In exploring the effect of uncertainty surrounding load models in assess- ment, a structural reliability analysis was conducted on three bridges in Chapter 4 to assess the effect of changing definitions of code-defined traf- fic loading on safety classifications of the structures. It was observed that earlier codes produced less onerous flexural load effects and, as such, resulted in a reduced demand for flexural capacity and thus reliability in- dices closer to that determined under the probabilistic load model; mak-

7.2 Detailed Results

ing them more susceptible to limit-state violation and a greater interven- tion burden. It was shown that bridges produced under loading prescribed by modern standards produced bridges with a higher β assessed under a probabilistic load model, and resulted in a significantly reduced expected life-cycle cost; despite the increased initial construction costs due to a higher minimum requirement for flexural reinforcement. This increased initial cost was seen to be significantly offset with a lower expected cost of failure over the bridges life-cycle. Given the disparity between β for the probabilistic load model and the more recent codes of practice, it is evident that, while bridge structures designed and constructed according to these standards should have a higher resistance capacity than seen in bridges designed to the extent of the earlier standards, the use of the load model itself for assessment does not reflect the true operating state of the bridge.

4. The prevalence of visual inspection based condition ratings in bridge maintenance management showed the potential for using the vast amount of information generated from these inspections to reduce network uncer- tainty by extracting patterns from a large data-set. In Chapter 5, methods from which to explore these large data-sets were shown to be principal

component analysis (PCA) and exploratory factor analysis (EFA); both data

reduction techniques to explore maximum variance in a data-set. In a comparison of the complexities of the methods, it was shown that PCA was a preferred method based on its absence of subjectivity in the input model, and it was shown that the level of data available for analysis was adequate for a reasonable assessment. In Chapter 6, the analysis was con- ducted in on data-sets of masonry arch bridges in Ireland and Portugal, as well as reinforced concrete bridges in Portugal, to exploit dimension- ality reduction techniques to establish latent variables. It was seen that there was good correlation in the PCs, where elements typically had the same influence on the PC across the two countries and both bridge types. Despite some directional variances in the PC coefficients αij between the

two data-sets, it was observed that the absolute and squared values of

αij were in general agreement across both regions. This demonstrates

how PCA can be an effective tool when looking for a direct comparison between the relative health or condition of elements in multiple data-sets. 5. In Chapter 6, it was observed that the analysis derived a latent variable

based on the conditions of the individual elements, and it was notable that when plotted against the overall condition ratings of the bridges, there was little correlation observed with the recorded values in the data-set; showing the subjectivity of overall condition rating scores assigned semi- independent of the condition of the bridges elements. Referring back to the original data-set and ranking the bridges according to their scores for this PC, it was seen that bridges with the lowest scores had elements in favourable conditions, while the bridges with the highest score showed advanced damage in each element. A new condition rating model based on weighting functions was proposed based on this latent variable, which was seen to perform at a higher level than existing overall structure classi- fications based on condition ratings. In addition, it was possible to create a refined distribution of the condition ratings for specific bridge types; which can enable an improved ranking of bridges in the network and bet- ter highlight at-risk bridges. By using the vast amounts of data currently available this way, it is possible to better inform decision making by using this data to reduce uncertainty levels surrounding maintenance activities dictated by condition rating data.