Artigos de Revista

This study proposes a methodology for developing deterioration models and predicting the service lives of vertical assets of urban water systems (i.e., water storage tanks and pumping stations) using regression analysis. The main factors contributing to the deterioration of these assets are analyzed. Simple and multiple linear regression models of average and maximum deterioration are calculated for 22 water storage tanks and 17 wastewater pumping stations. Data on a set of four water storage tanks are used to validate the developed deterioration models. Service life prediction is carried out using the developed models and considering two maximum deterioration levels: the maximum recommended and admissible deterioration levels. Two water storage tanks are further studied to illustrate and discuss the effect of maintenance and rehabilitation interventions on asset service life by comparing the asset deterioration before and after the interventions.

Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%

ABSTRACT - The ultimate capacity of pultruded glass fibre reinforced polymer (pGFRP) profiles depends significantly on geometrical imperfections (GIs), given their sensitivity to buckling phenomena arising from both thin walls and low elastic moduli. However, GIs are not yet comprehensively addressed in design guidance. This paper proposes a new approach to characterize the initial GIs of pGFRP profiles based on a modal approach. Given the lack of comprehensive knowledge in this area, this study presents a highly accurate and robust methodology to measure GIs and dimensional deviations (DDs) in pGFRP profiles using a 3D contact coordinate measurement machine (CMM). The modal approach encompasses the measurement of dimensional parameters and a point cloud transformation that enables the assessment of GIs associated with pure buckling modes of pGFRP profiles. This procedure allows the quantification of three types of global GIs associated to (i) minor-axis (weak axis), (ii) major-axis (strong axis) bending, and (iii) twist. Additionally, the procedure also includes the assessment of local GIs, considering the wall (plate-like) imperfections. The separation of GIs into these four types (shape and amplitude) is of major relevance as its paves the way to the development of analytical design formulas for the strength prediction of pGFRP members. The approach described in this paper also serves two important purposes: (i) the statistical analysis of DDs and GIs of pGFRP members, and (ii) the identification of distinct shapes and amplitudes of GIs that form the basis for reliable design considerations of pGFRP members.