Methodology for the service life prediction of ceramic claddings in pitched roofs

Ramos, Rui; Silva, Ana; Brito, Jorge de; Gaspar, Pedro Lima

doi:10.1016/j.conbuildmat.2018.01.111

Cited by 23 publications

(11 citation statements)

References 40 publications

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“…The two-scale classification system implemented to describe the degradation process of CCPR is presented in Table 1 [32]. This classification system is based on the work of Gaspar and de Brito [33].…”

Section: Maintenance Modelmentioning

confidence: 99%

“…As mentioned above, MS1-only total replacement; MS2-composed of total replacement and minor intervention and MS3-composed of total replacement, minor intervention and cleaning In terms of service life, it is assumed here that degradation condition D signifies the end of the service life (ESL) [32]. In the model, the probability of 50% between conditions C and D defines the ESL.…”

Section: Maintenance Strategies Assumptions and Costsmentioning

confidence: 99%

“…In this way, the author developed a method that couples the net present value assessment and a service life prediction system. More recently, Ramos et al [32], with the aim of developing a methodology to predict the service life of pitched roofs with ceramic tiles as external cladding, adopted a simple regression analysis to obtain the degradation curves. These curves were fitted to the sample collected during a fieldwork survey.…”

Section: Introductionmentioning

confidence: 99%

“…Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 21 durability of this building element[21,32,[51][52][53][54][55][56] but still within the order of magnitude of the values suggested. Furthermore, it is noted that the sample analysed corresponds to a homogeneous design and execution characteristics, since the claddings in the database are owned by the Portuguese Air Force, and when the pitched roofs with ceramic tiles are properly built and maintained, their longevity and durability can be quite high[57].…”

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confidence: 99%

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Maintenance Modelling of Ceramic Claddings in Pitched Roofs Based on the Evaluation of Their In Situ Degradation Condition

et al. 2020

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Existing maintenance policies have several limitations, mainly due to the lack of knowledge regarding the durability and performance of buildings. Usually, the maintenance policies are insufficiently accurate, neglecting the risk of failure over time and the global costs associated with repairs. In this study, a condition-based maintenance model, based on Petri nets, is proposed to evaluate the impact of three maintenance strategies of ceramic claddings in pitched roofs (CCPR): MS1—only total replacement; MS2—composed of total replacement and minor intervention and MS3—composed of total replacement, minor intervention and cleaning operations. In this study, 146 CCPR were inspected in situ, with a total area of 43,991.6 m2. The remaining service life of the CCPR; the global costs over the claddings’ lifetime (considering inspection, maintenance, replacement and disposal costs); the claddings’ degradation condition and the number of replacements during the time horizon are used to evaluate the performance of the different maintenance strategies through a simplified multi-criteria analysis. The results show that the gains in performance, in terms of expected service life and durability, of the consideration of preventive maintenance actions (minor interventions or cleaning operations) outweigh the increase of the operation costs.

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Section: Maintenance Modelmentioning

confidence: 99%

Section: Maintenance Strategies Assumptions and Costsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Maintenance Modelling of Ceramic Claddings in Pitched Roofs Based on the Evaluation of Their In Situ Degradation Condition

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“…The condition of infrastructures experiences accelerating deterioration over time as a result of many factors such as deferred maintenance actions, excessive loads, poor construction practices, inadequate buildings materials and cycles of freeze and thaw (Ramos et al , 2018; Dong, 2018). In the United States, bridges are vulnerable to high levels of deterioration.…”

Section: Introductionmentioning

confidence: 99%

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

Abdelkader

2021

SASBE

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PurposeCracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.Design/methodology/approachThis paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.FindingsIt was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.Originality/valueLiterature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.

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The Way Forward

Brito

Pereira

Silvestre

et al. 2020

Expert Knowledge-Based Inspection Systems

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Methodology for the service life prediction of ceramic claddings in pitched roofs

Cited by 23 publications

References 40 publications

Maintenance Modelling of Ceramic Claddings in Pitched Roofs Based on the Evaluation of Their In Situ Degradation Condition

Maintenance Modelling of Ceramic Claddings in Pitched Roofs Based on the Evaluation of Their In Situ Degradation Condition

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

The Way Forward

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