From Conventional to Sustainable SHM: Implementation of Artificial Intelligence in the Department of Civil Engineering, University of Malaya - Details

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Abstract：

Computer-based　technologies　and　their　applications　pervade　everywhere　in　real　life,　especially　in　different　fields　of　civil　engineering.　For　example,　conventional　structural　health　monitoring　(SHM)　has　been　rapidly　upgraded　to　sustainable　SHM　using　artificial　intelligence.　It　is　because　conventional　approaches　are　challenged　by　real-time,　low-cost,　and　quality-guaranteed　SHM.　In　this　direction,　a　number　of　innovative　researches　have　been　carried　out　in　the　Department　of　Civil　Engineering,　University　of　Malaya.　This　paper　attempts　to　present　the　latest　developments　of　SHM-based　artificial　intelligence　in　Structural　Health　Monitoring　Research　Group　(StrucHMRSGroup)　and　Advance　Shock　and　Vibration　Research　Group　(ASVR).　To　this　end,　the　applications　of　artificial　neural　networks,　fuzzy　logic,　genetic　algorithm,　data　mining,　and　regression　analysis　in　SHM　are　presented　with　the　aim　of　showing　the　efficiency　of　these　methods.　©　2021　IEEE.

Keyword：

Data mining Fuzzy logic Fuzzy neural networks Genetic algorithms Regression analysis Structural health monitoring

Community：

[ 1 ] [Gordan, Meisam]University of Malaya, Department of Civil Engineering, Kuala Lumpur, Malaysia
[ 2 ] [Ghaedi, Khaled]Research and Development Centre, Pasofal Engineering Group, Kuala Lumpur, Malaysia
[ 3 ] [Ismail, Zubaidah]University of Malaya, Department of Civil Engineering, Kuala Lumpur, Malaysia
[ 4 ] [Benisi, Hamed]Fuzhou University, Department of Water Resources and Harbor Engineering, Fuzhou, China
[ 5 ] [Hashim, Huzaifa]University of Malaya, Department of Civil Engineering, Kuala Lumpur, Malaysia
[ 6 ] [Ghayeb, Haider Hamad]University of Malaya, Department of Civil Engineering, Kuala Lumpur, Malaysia