GPR Identification of Prestressing Tendons in Areas With High Density of Ordinary Reinforcement.

Owerko, Piotr

doi:10.5593/sgem2013/ba1.v2/s05.012

Cited by 3 publications

(1 citation statement)

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“…mutual collisions of loose hangers of arch bridges in dynamic tests), cracks, local detachments of concrete cover etc. [20,21,22], -validation of the assumptions of numerical FE models analyzed both in the longitudinal and transverse directions as well as in the context of the dynamic response of the model / calibration of the FEM model -replacement of the FEM model class [23], -load capacity validation [9], -reliability validation [24], -fatigue reliability validation [25], -initial calibration of the SHM system [26], including the creation of an appropriate reference point and determination of alarm thresholds, -reference base for subsequent measurements [27]. It should, however, be emphasized, that all of the above objectives can be achieved much more reliably in the view of the existence and possibility of insight into the statistics from analogous results of the mechanical response of similar bridges that have been studied in the past.…”

Section: Purposefulness Of Bridge Load Testsmentioning

confidence: 99%

A Proposal to Facilitate Mandatory Bridge Load Tests with Artificial Neural Network Analyses Using a Digital Data Aggregation Platform

Owerko¹,

Kałuża²,

Wazowski³

2021

Architecture, Civil Engineering, Environment

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The paper presents an innovative concept of digital aggregation of data related to mandatory in-situ load tests of bridge structures. The proposed approach allows to manage various types of information regarding those experiments, in a way which is consistent with current good practises in BIM technology and digitalisation of construction industry. The proposed web platform will allow for vast improvements in decision-making process regarding admission of a given bridge for service, in proper analyses and even predictions of bridges mechanical response. Initial architecture of the system is introduced along with an appropriate literature review and the identification of key actors and their roles in the described information management process. To highlight the potential of the solution, two examples are shown. In both cases key advantages of digital aggregation are emphasised: the possibility to learn from previous analogical in-situ experiments, and the possibility to utilise modern machine learning algorithms and state-of-the-art open-source solutions.

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