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Article

Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge

1
School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
2
Amey Consulting, Forth Road Bridge, Administration Building, South Queensferry EH30 9QZ, UK
3
School of Natural and Built Environment, Queen’s University Belfast, David Keir Building, Belfast BT9 5AG, UK
4
Civil Infrastructure Technologies for Resilience and Safety, University of Central Florida, Orlando, FL 32816-2450, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5423; https://doi.org/10.3390/app10165423
Received: 10 July 2020 / Revised: 31 July 2020 / Accepted: 3 August 2020 / Published: 5 August 2020
(This article belongs to the Section Civil Engineering)
This paper examines the nature of traffic loading in recurrent congested traffic conditions on a long-span suspension bridge. Traffic flow and percentage of trucks are extracted from image data and a cluster analysis performed to classify the data into four clusters. One cluster (MTHF, medium truck percentage and high flow) is identified that incorporates almost 50% of the hours of traffic data scattered throughout the day. Site-specific load assessment confirms that this MTHF cluster is the most critical for the bridge considered, the Forth Road Bridge in Scotland. For non-recurrent congestion, another cluster (HTLF, high percentage of trucks and low flow) is shown to govern but this finding is highly site-specific, depending on the relative frequency of the different types of congestion. A comparison of the maximum hourly/daily MTHF load effect of the cable force for five notional bridges shows that a 100% increase in the bridge span generates an increase of about 65% in the characteristic load effect. View Full-Text
Keywords: bridge; cluster; congestion; dendrogram; flow; image; loading; long-span; non-recurrent; recurrent; suspension; traffic bridge; cluster; congestion; dendrogram; flow; image; loading; long-span; non-recurrent; recurrent; suspension; traffic
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MDPI and ACS Style

Micu, E.A.; OBrien, E.J.; Malekjafarian, A.; McKinstray, R.; Angus, E.; Lydon, M.; Catbas, F.N. Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge. Appl. Sci. 2020, 10, 5423. https://doi.org/10.3390/app10165423

AMA Style

Micu EA, OBrien EJ, Malekjafarian A, McKinstray R, Angus E, Lydon M, Catbas FN. Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge. Applied Sciences. 2020; 10(16):5423. https://doi.org/10.3390/app10165423

Chicago/Turabian Style

Micu, E. A., Eugene J. OBrien, Abdollah Malekjafarian, Ross McKinstray, Ewan Angus, Myra Lydon, and F. N. Catbas 2020. "Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge" Applied Sciences 10, no. 16: 5423. https://doi.org/10.3390/app10165423

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