García-Sánchez, DavidIglesias, FranciscoDiez, JesusPiñero, IñakiFernández-Navamuel, AnaSánchez, Diego ZamoraJiménez-Fernandez, José CarlosRizzo, PiervincenzoMilazzo, Alberto2021García-Sánchez , D , Iglesias , F , Diez , J , Piñero , I , Fernández-Navamuel , A , Sánchez , D Z & Jiménez-Fernandez , J C 2021 , Gradient-Boosting Applied for Proactive Maintenance System in a Railway Bridge . in P Rizzo & A Milazzo (eds) , unknown . vol. 127 , 2366-2557 , Springer, Cham , pp. 236-244 , European Workshop on Structural Health Monitoring, EWSHM 2020 , 6/07/20 . https://doi.org/10.1007/978-3-030-64594-6_24conference978-3-030-64593-99783030645939978-3-030-64594-6researchoutputwizard: 11556/1056Publisher Copyright: © 2021, Springer Nature Switzerland AG.This article contributes in the research direction of the application of Machine Learning techniques in bridge safety assessment and it lays basis to further improve the accuracy of safety assessment including analysis of real data. The communication puts forward the process and model of scale measured points correlation of bridge monitoring system on the frequency domain as a tactic to control the influence of a railway device (crossing) located on the top deck of a railway bridge. The process and model are put forward mainly for the characteristics of the damage detection for long-term assessment, going from an intensive multi-sensor monitoring system to a softer one. Finally, a Gradient-Boosting multi-regressor method has been developed to be easily implemented in a warning system that provides predictive skills to the current preventive maintenance strategy. The method is validated by simulating the undamaged and abnormal scenarios with Monte Carlo method.91820552enginfo:eu-repo/semantics/restrictedAccessconference output10.1007/978-3-030-64594-6_24Gradient-boostingCorrelationMulti-sensorBridgeGradient-boostingCorrelationMulti-sensorBridgeCivil and Structural EngineeringProject IDinfo:eu-repo/grantAgreement/EC/H2020/690660/EU/Risk based approaches for Asset inteGrity multimodal Transport Infrastructure ManagEment/RAGTIMEinfo:eu-repo/grantAgreement/EC/H2020/769373/EU/Future proofing strategies FOr RESilient transport networks against Extreme Events/FORESEEinfo:eu-repo/grantAgreement/EC/H2020/690660/EU/Risk based approaches for Asset inteGrity multimodal Transport Infrastructure ManagEment/RAGTIMEinfo:eu-repo/grantAgreement/EC/H2020/769373/EU/Future proofing strategies FOr RESilient transport networks against Extreme Events/FORESEEFunding InfoThe work presented here has received funding from Horizon 2020, the EU’s Framework Programme for Research and Innovation, under grant agreement number 690660 (Project: RAGTIME), and also under grant agreement number 769373 (Project: FORESEE).The work presented here has received funding from Horizon 2020, the EU’s Framework Programme for Research and Innovation, under grant agreement number 690660 (Project: RAGTIME), and also under grant agreement number 769373 (Project: FORESEE).http://www.scopus.com/inward/record.url?scp=85101194790&partnerID=8YFLogxK