Inteligência artificial. Lisboa: Fundação Francisco Manuel dos Santos, 2018; col. Ensaios da Fundação. Mentes digitais: a Ciência Redefinindo a Humanidade. Lisboa: Instituto Superior Técnico Press, 2017 (1.ª ed; 2.ª ed. 2018)

Silva, Armando Malheiro da

doi:10.21747/16463153/39r1

Cited by 1 publication

(2 citation statements)

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“…The “convex‐shaped” pattern is characterized by the physical and chemical phenomena that initially act slowly, but whose action is felt cumulatively. The “concave‐shaped” pattern is more related to biological factors that, at an early stage, develop rapidly, but whose potential for deterioration decreases over time (Silva, 2015). The S‐shaped pattern is associated with phenomena that change in intensity over time.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The most common methods are: (a) deterministic; (b) probabilistic; and (c) engineering (Lacasse & Sjöström, 2004; Moser, 2004). Deterministic methods are based on the analysis of asset deterioration factors and their mechanisms to quantify them in terms of deterioration models (Silva, 2015; Tavares et al., 2020). These methods are easy to understand and apply; they can be implemented relatively quickly, avoiding redundancy of information; they maintain their operability even when not all variables of the same problem are known (Gaspar, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Deterioration Models and Service Life Prediction of Vertical Assets of Urban Water Systems

Cabral,

Loureiro,

Amado

et al. 2024

Water Resources Research

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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. Results include simple linear regression models of average and maximum deterioration indices as a function of asset age and multiple linear regression models that incorporate other physical, operational and environmental factors. The results show that simple linear regression models of asset deterioration show a better predictive power than multiple regression models. Despite the higher data variability of multiple regression models, these models allow to include the random process of asset deterioration, through the calculation of the standard deviation. This study also shows that periodic interventions are a preferable maintenance and rehabilitation strategy over major sporadic rehabilitation interventions since it allows to maintain assets in good condition and to extend their service life almost indefinitely.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%