Documents

Noemi Jiménez-Redondo, Alvaro Calle-Cordón, et. al. “Improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques (INFRALERT)”. BESTInfra 2017.

Álvaro Calle-Cordón, Noemi Jiménez-Redondo, et. al. “Integration of RAMS in LCC analysis for linear transport infrastructures. A case study for railways” (BESTInfra 2017)

Francisco J. Morales, Antonio, Reyes, et. al. “Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach”. BESTInfra 2017.

Ute Kandler. “Decision Support for Tactical Planning of Maintenance Activities – a use case for the INFRALERT project”. International Conference on Operations Research (OR2017) (Berlín, Sep 2017)

Poster presented at Transportation Research Board (TRB) 97th Annual Meeting (January, 2018)

F.J. Morales, A. Reyes, N. Cáceres, L. Romero, F.G. Benítez, “Automatic prediction of maintenance interventions types in roads using machine learning and historical records” (Transportation Research Record (Journal of the Transportation Research Board, January 2018)

Ute Kandler, Axel Simroth, et al. “Decision support for tactical planning – A use case of the INFRALERT project”. TRA 2018 (Vienna, April 2018)

Poster presented at Transport Resesarch Arena (TRA2018) (Vienna, April 2018)

Noemi Jiménez et al. “INFRALERT: improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques” (Vienna, TRA 2018, April 2018)

Álvaro Calle, Noemi Jiménez, et al. “Combined RAMS and LCC analysis in railway and road transport infrastructures”. TRA 2018 (Vienna, April 2018)

Antonio Reyes, Francisco J. Morales, et. al. “Automatic prediction of maintenance intervention types in transport linear infrastructures using machine learning”. Paper & Poster presented at TRA 2018 (Vienna, April 2018)

D3.1 Segregation of linear asset information in different hierarchies for nowcasting and forecasting (summary sheet)

D1.1 Requirements and targets of the INFRALERT subsystems (summary sheet)

D1.2 Set up of case studies and Evaluation Framework (summary sheet)

D4.1 Database relevant to alert estimation (summary sheet)

D6.1 Smart Decision Support Framework (summary sheet)

D6.1 Smart Decision Support Framework (full deliverable)

D5.1 Report on RAMS data collection and failure rate analysis at component level (summary sheet)

D5.1 Report on RAMS data collection and failure rate analysis at component level (full deliverable)

D9.2 Dissemination and Communication Plan (summary sheet)

D7.1 Architectural and Functional Definition of eIMS (summary sheet)

D2.1 Data ontology and architecture. Abstract data types definition. Data localization architecture (summary sheet)

D4.3 Methodologies and procedures for inferring three-dimensional alert-severity-intervention pattern space. Supervised and unsupervised approaches (summary sheet)

D9.3 Dissemination & Communication Activities. Mid-Term Report (summary sheet)

D5.2 Report on RAMS&LCC integrated models (summary sheet)

D4.3 Methodologies and procedures for inferring three-dimensional alert-severity-intervention pattern space. Supervised and unsupervised approaches (full deliverable)

D3.2 Application of physical, data-driven and symbolic models from component to system level (summary sheet)

D3.2 Application of physical, data-driven and symbolic models from component to system level (full deliverable)

D4.2 Asset alert level estimator (summary sheet)

D6.2 Report on models and algorithms for decision support (summary sheet)

D6.2 Report on models and algorithms for decision support (full deliverable)

D2.2 Implementation and working prototype, exposing the functionalities via the web (summary sheet)

D5.3 A methodology for RAMS&LCC analysis in transport infrastructure (summary sheet)

D6.3 Report on a tool for strategic decision assessment (summary sheet)

D3.3 Hybrid methodology in different asset levels: Contribution to more accurate asset forecasting and nowcasting (summary sheet)

D4.4 Asset severity level predictor (summary sheet)

INFRALERT Newsletter No.1

INFRALERT Newsletter No.2

INFRALERT Newsletter No.3

INFRALERT Leaflet No.1

INFRALERT Leaflet No.2

INFRALERT press release no.1 (01.05.15) – Official start of the project

INFRALERT press release no.1 (Spanish) (01.05.15) – Comienzo oficial del proyecto

INFRALERT press release no.2 (19.06.15) – Kick-off meeting

INFRALERT press release no.3 (16.07.15) – Website launched

INFRALERT Press Release no.4 (02.10.15) – 1st EAB Workshop

João Morgado, Emanuel Duarte. “O projeto infralert: apresentação e primeriros desenvolvimentos” – 8th Portuguese Road Congress (Lisbon – April, 2016)

Noemi Jiménez-Redondo, Alvaro Calle-Cordón, et. al. “Improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques (INFRALERT)”. BESTInfra 2017.

Álvaro Calle-Cordón, Noemi Jiménez-Redondo, et. al. “Integration of RAMS in LCC analysis for linear transport infrastructures. A case study for railways” (BESTInfra 2017)

Francisco J. Morales, Antonio, Reyes, et. al. “Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach”. BESTInfra 2017.

F.J. Morales, A. Reyes, N. Cáceres, L. Romero, F.G. Benítez, “Automatic prediction of maintenance interventions types in roads using machine learning and historical records” (Transportation Research Record (Journal of the Transportation Research Board, January 2018)

Ute Kandler, Axel Simroth, et al. “Decision support for tactical planning – A use case of the INFRALERT project”. TRA 2018 (Vienna, April 2018)

Noemi Jiménez et al. “INFRALERT: improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques” (Vienna, TRA 2018, April 2018)

Álvaro Calle, Noemi Jiménez, et al. “Combined RAMS and LCC analysis in railway and road transport infrastructures”. TRA 2018 (Vienna, April 2018)

Antonio Reyes, Francisco J. Morales, et. al. “Automatic prediction of maintenance intervention types in transport linear infrastructures using machine learning”. Paper & Poster presented at TRA 2018 (Vienna, April 2018)