M. Dirhamsyah, Israr B.M. Ibrahim, Syarizal Fonna, Teuku Arriessa Sukhairi, Hammam Riza, Syifaul Huzni
Motivated by the accelerated development of Artificial Intelligence technologies, the government of Indonesia formulated a National Strategic Plans of Artificial Intelligence (Renstranas KA). Two top priorities pursued by the plan are AI technologies for disaster risk management and smart city. On another hand, aging and degradation of reinforced concrete infrastructures are two factors that increase the risk of structural failures and decrease infrastructure resilience in developing countries. A primary mechanism for these factors are steel rebar corrosion inside reinforced concrete structures. In this paper, we present an AI approach for structural corrosion monitoring. We also present the technical challenges and proposed resolutions toward achieving automated, real-time corrosion monitoring in infrastructures as part of disaster risk management in a smart city setting. © 2022 IEEE.
Universitas Syiah Kuala Banda, Dept of Mechanical and Industrial Engineering, Aceh, Indonesia; CCRG Lab Universitas Syiah Kuala Banda, Aceh, Indonesia; Badan Riset dan Inovasi Nasional (BRIN), Jakarta, Indonesia
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