Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
ST18: 7.1 Transport Infrastructure: Application of Machine Learning
Time:
Tuesday, 17/Apr/2018:
3:15pm - 4:45pm

Session Chair: Manfred Haider, AIT
Location: Stolz 1

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Presentations
PaperAcceptPODIUM

Improving Linear Transport INFRAstructure Efficiency by Automated LEaRning and Optimised Predictive Maintenance Techniques (INFRALERT)

Noemi Jiménez-Redondo1, Álvaro Calle Cordón1, Ute Kandler2, Axel Simroth2, Francisco Morales3, Antonio reyes3, Johan Odelius4, Stephen Famurewa4, João Morgado7, Emanuel Duarte7, Daniele Iorio5, Marco Fruttero5, András Juszt6

1CEMOSA, Spain; 2Fraunhofer IVI, Germany; 3University of Sevilla, Spain; 4Luleå University of Technology, Sweden; 5DMA, Italy; 6Regens, Hungary; 7Infraestruturas de Portugal, Portugal


PaperAcceptPODIUM

A machine learning approach for maintenance prediction of railway assets

Zaharah Allah Bukhsh1, Aaqib Saeed2, Irina Stipanovic1

1Department of Construction Management and Engineering, University of Twente, Netherlands; 2Department of Computer Science, University of Twente, Netherlands


PaperAcceptPODIUM

Bridge scour monitoring technique using the vibratory response of rods embedded in the riverbed

NISSRINE BOUJIA1, FRANZISKA SCHMIDT1, CHRISTOPHE CHEVALIER1, DOMINIQUE SIEGERT1, DAMIEN PHAM VAN BANG2

1IFSTTAR, France; 2CEREMA, France


PaperAcceptPODIUM

Turning data driven condition now- and forecasting for railway switches into maintenance actions

Daniela Narezo Guzman1, Edin Hadzic2, Robert Schuil2, Eric Baars2, Jörn Christoffer Groos1

1German Aerospace Center DLR, Germany; 2Strukton Rail, Netherlands


PaperAcceptPODIUM

Analysis of pressure loads on installations in high-speed tunnels- with model scale experiments and numerical simulations

Helmut Kühnelt1, Alois Vorwagner1, Daniela Heine2, Florian Saliger3, Roman Heissenberger3

1Austrian Institute of Technology GmbH, Austria; 2DLR German Aerospace Center; 3OEBB Infrastruktur AG


PaperAcceptPODIUM

Skin-like Sensor for Structural Health Monitoring of Transport Infrastructure

ADEWOLE ADESIYUN1, ANGELOS AMDITIS2, ATHANASIOS ANASTASOPOULOS9, STEPHANOS CAMARINOPOULOS5, CLAUDIA CIUCA1, YANNIS DAMIGOS2, BERND FRANKENSTEIN7, JOHANNES GANTNER8, REIMUND GERHARD3, PETER JONES6, VASSILIS KALIDROMITIS4, KONSTANTINOS LOUPOS2, PANAGIOTIS PANETSOS10, DMITRY RYCHKOV3, WERNER WIRGES3, RAHSAN YILDIRIM11

1Forum of European National Highway Research Laboratories, Belgium; 2Institute of Communication and Computer Systems, Greece; 3University of Potsdam, Institute of Physics and Astronomy, Germany; 4Techniche e consulenze nell'ingegneria civile consulting engineers spa., Italy; 5Risa Sicherheitsanalysen GmbH, Germany; 6Transport Research Laboratories, UK; 7Teletronic Rossendorf GmbH, Germany; 8University of Stuttgart, Chair of Building Physics, Germany; 9Mistras Group Hellas, Greece; 10Egnatia Odos A.E., Greece; 11Turkish General Directorate of Highways, Turkey


PaperAcceptPOSTER

Automatic Prediction of Maintenance Intervention Types in Transport Linear Infrastructures using Machine Learning

Antonio Reyes1, Francisco J. Morales1, Noelia Caceres2, Luis M. Romero1, Francisco G. Benitez1, Joao Morgado3, Duarte Emanuel3, Martins Teresa3

1School of Engineering, University of Sevilla, Sevilla, Spain; 2AICIA, School of Engineering, Sevilla, Spain; 3Infrastruturas de Portugal, Almada, Portugal


PaperAcceptPOSTER

Machine Learning Algorithms for Rock Cutting Slopes Stability Condition Identification

Joaquim Tinoco1, António Gomes Correia1, Paulo Cortez1, David Toll2

1University of Minho, Portugal; 2University of Durham, UK


PaperAcceptPOSTER

Road Surface Characterization using Crowdsourcing Vehicles

Boaz Mizrachi1, Shimon Nesichi2, Aviv Rozenberg1, Stanislav Shapira1

1MobiWize, Israel; 2Chief Scientist, Netivei Israel – National Transport Infrastructure Company



 
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