An optimal condition based maintenance scheduling for metal structures based on a multidisciplinary research approach.

Latif, J., Khan, Z. A., Nazir, H., Stokes, K. and Smith, R., 2019. An optimal condition based maintenance scheduling for metal structures based on a multidisciplinary research approach. Structure and Infrastructure Engineering. (In Press)

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Abstract

Latest research findings show that the deterioration of metal coatings results due to complex combination of material and meteorological parameters. The classical maintenance scheduling do not consider complex interface of materials and meteorological parameters to determine optimal maintenance framework. The cost of recoating can be optimised through appropriate selection of coating specifications and maintenance strategy. This research provides a multidisciplinary algorithmic approach to determine cost-effective solutions for recoating. The specifications of red oxide primer coating and structural steel substrate system are considered for simulation analysis. The results show that the appropriate selection of 10% increase in coating thickness based on coating-substrate system specifications resulted in 20-25% reduction in annual patch failures which reduces 5-6 % cost of recoating. Furthermore, the proposed model also simulated to compare Patch recoating and Part recoating strategy and algorithm show that the Part recoating is cost-effective as compared to Patch recoating if number of annual patch failure is greater than ‘2’ and area of the part is ‘2x’times larger than the area of the patch. Contrary, the Patch recoating results in low cost if the part area is ‘10x’ times larger than patch area and number of annual patch failures are less than ‘7’.

Item Type:Article
ISSN:1573-2479
Additional Information:Funded by Ulterius Corrosion Health Monitoring (USHM) using Wireless Sensor Techniques
Uncontrolled Keywords:Structures ; Maintenance strategy ; Recoating cost ; Blistering ; Condition based maintenance ; Predictive modelling ; Corrosion cost
Group:Faculty of Science & Technology
ID Code:31825
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Feb 2019 11:12
Last Modified:20 Feb 2019 16:03

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