Development of methodologies for damage identification in railway bridges based on indirect monitoring and machine learning algorithms

• Grant number: 22/13045-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: April 01, 2023
• End date: March 31, 2027
• Field of knowledge: Engineering - Civil Engineering - Structural Engineering
• Principal Investigator: Tulio Nogueira Bittencourt
• Grantee: Cassio Scarpelli Cabral de Braganca
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Bridges and viaducts are key elements of any land transport infrastructure, allowing for shorter and more economical journeys by avoiding the need to bypass obstacles such as rivers and valleys. This aspect is even more important when it comes to the railway in which vehicles are not able to overcome very intense slopes. Recent technological advances in trains have led to significant increases in speeds and axle load significantly increasing demands on bridges and viaducts. These increases in demand, linked to the aging of the infrastructure, are making it increasingly important to have robust methodologies for the early detection of damage in these structures. Nowadays, the standard, both in Brazil and in the world, is still the performance of purely visual periodic inspections, which does not allow the early detection of many types of damage. Faced with this reality, some infrastructure managers have chosen to install modern structural integrity monitoring systems on bridges. The high cost associated with these systems, however, ends up making their installation on all bridges and viaducts of a railroad prohibitive, limiting their use to those that are more critical or of greater length. In an attempt to address this deficiency, infrastructure managers are increasingly interested in new technologies capable of detecting road damage based on monitoring systems embedded in rail vehicles. Faced with this motivation, this work proposes the development of an automatic methodology for the detection of damage in railway bridges and viaducts based on the recognition, through machine learning techniques, of characteristic patterns of damage in dynamic responses obtained through in-vehicle monitoring systems.

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BRAGANCA, CASSIO; SOUZA, EDSON F.; RIBEIRO, DIOGO; MEIXEDO, ANDREIA; BITTENCOURT, TULIO N.; CARVALHO, HERMES. Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review-Part II: Track and Vehicle. APPLIED SCIENCES-BASEL, v. 13, n. 12, p. 29-pg., 2023-06-01. (22/13045-1)

SOUZA, EDSON F.; BRAGANCA, CASSIO; MEIXEDO, ANDREIA; RIBEIRO, DIOGO; BITTENCOURT, TULIO N.; CARVALHO, HERMES. Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review-Part I: Bridges and Viaducts. APPLIED SCIENCES-BASEL, v. 13, n. 12, p. 31-pg., 2023-06-01. (22/13045-1)

DE SOUZA, EDSON FLORENTINO; BRAGANCA, CASSIO; RIBEIRO, DIOGO; BITTENCOURT, TULIO NOGUEIRA; CARVALHO, HERMES. Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders. RAILWAY ENGINEERING SCIENCE, v. N/A, p. 28-pg., 2024-08-29. (22/13045-1)

GAMINO, ANDRE LUIS; HUNE, RAFAEL PETILLE; SANTOS, RUAN RICHELLY; DE BRAGANCA, CASSIO SCARPELLI CABRAL; BITTENCOURT, TULIO NOGUEIRA; CARVALHO, HERMES; FUTAI, MARCOS MASSAO. Dynamic analysis of a pedestrian bridge using monitoring and computational techniques. Structure and Infrastructure Engineering, v. N/A, p. 19-pg., 2024-11-04. (22/13045-1)

RUIZ, DIANELYS VEGA; DE BRAGANCA, CASSIO SCARPELLI CABRAL; PONCETTI, BERNARDO LOPES; BITTENCOURT, TULIO NOGUEIRA; FUTAI, MARCOS MASSAO. Vibration-based structural damage detection strategy using FRFs and machine learning classifiers. STRUCTURES, v. 59, p. 14-pg., 2023-12-15. (22/13045-1)

RUIZ, DIANELYS VEGA; DE BRAGANCA, CASSIO SCARPELLI CABRAL; PONCETTI, BERNARDO LOPES; BITTENCOURT, TULIO NOGUEIRA; FUTAI, MARCOS MASSAO. Structural damage detection for a small population of nominally equal beams using PSO-optimized Convolutional Neural Networks. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, v. 225, p. 20-pg., 2024-12-28. (22/13045-1)