New technologies for bridge inspection and monitoring: a perspective from European Union research and innovation projects

Gkoumas, Konstantinos; Stepniak, Marcin; Cheimariotis, Ilias; Marques dos Santos, Fabio

doi:10.1080/15732479.2024.2311898

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…Currently, regarding artificial intelligence models, infrastructure managers are implementing, along with continuous monitoring, the use of drones or unmanned aircraft systems (UASs) that allow the detection of degraded conditions that are not possible by visual human inspections due to being inaccessible or due to limitations of human vision [ 33 , 34 , 35 ]. It is interesting to combine both technologies according to their synergies in order to consolidate a holistic maintenance system.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Degraded Infrastructure Conditions for Railway Operation

Sanz Bobi,

Garrido Martínez-Llop,

Rubio Marcos

et al. 2024

Sensors

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In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks).

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Section: Discussionmentioning

confidence: 99%

Prediction of Degraded Infrastructure Conditions for Railway Operation

Sanz Bobi,

Garrido Martínez-Llop,

Rubio Marcos

et al. 2024

Sensors

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“…The above is related to the stability assessment analysis due to the economic and social consequences of the failure. It is an input for the SIGP prioritization module related to investment plans for the rehabilitation, reconstruction, or replacement of existing bridges of the national road network [22,[28][29][30][31].…”

Section: Areas Of Evaluationmentioning

confidence: 99%

“…Finally, the collected data should be exported to a central geodatabase in the cloud. This development is part of a line of research around the world where technological applications are being implemented to support visual inspection and damage detection on bridges [30,[33][34][35][36][37][38]. The time during which this application should be used in the field depends on the index of each bridge, as explained in Table 3.…”

Section: Damage Catalogmentioning

confidence: 99%

A New Module for the Evaluation of Bridges Based on Visual Inspection through a Digital Application Linked to an Up-to-Date Database of Damage Catalog for Colombia

Muñoz-Diaz,

Vargas-Luna,

Nuñez-Moreno

et al. 2024

Buildings

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Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data.

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