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Long-term structural health monitoring of suramadu bridge
Salami M.R.a, Jesus A.a, Pamuncak A.P.b, Zarkasi I.b, Suhendro B.c, Budiono B.d, Suprobo P.e, Laory I.a
a University of Warwick, United Kingdom
b Indonesian Ministry of Public Works and Housing, Indonesia
c Gadjah Mada University, Indonesia
d Bandung Institute of Technology, Indonesia
e Sepuluh Nopember Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.Infrastructure systems deteriorate while in service due to routine use and exposure to extreme conditions (e.g. corrosive environment and dynamic loads). Many countries tackle with the serviceability of their infrastructure and failing to raise sufficient funding to repair and replace them. In order to optimize the maintenance, long-term reliability and life-cycle cost of infrastructure systems, Structural Health Monitoring (SHM) has received significant public attention in recent years. This paper reports on SHM and data measurement of the Suramadu Bridge, in Indonesia. Data from accelerometer, anemometer, thermocouple, tiltmeter, weight-in-motion (WIM) and electro-magnetic (EM) sensors were recorded since 2012 to 2015, considered in this paper which provide an extensive database for long-term monitoring of the bridge. In addition, a fully detailed finite element (FE) model was developed using SAP2000 and its dynamic behaviour compared with modal analysis. This information collected from the monitoring system in parallel with the FE model can be used for anomaly detection, performance assessment and maintenance planning of the bridge.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Corrosive environment,Dynamic behaviours,Extreme conditions,Infrastructure systems,Long term monitoring,Maintenance planning,Performance assessment,Structural health monitoring (SHM)[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This study is funded by the British Council (Grant ID: 217544274). The writers would also like to acknowledge the supports from the Indonesian Ministry of Public Works and Housing (IMPWH) and the University of Warwick, UK. Any opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the IMPWH.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]