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Agregacja bazodanowa i nauczanie głębokie systemu do zarządzania obiektami mostowymi w Polsce

Owerko Piotr, Kałuża Jerzy, Wazowski Marek

Mosty

2022

nr 1

54--58

Praca stanowi ukazanie koncepcji budowy nowego systemu do zarządzania obiektami mostowymi w Polsce. Może on stanowić niezwykle wartościowe narzędzie wspierające procesy decyzyjne dotyczące konkretnych mostów – np. dotyczące potrzeby ich remontów, wzmocnień, wprowadzenia monitoringu SHM czy rozbiórki. System ten ma być oparty na filozofii „ukierunkowania na dane” (ang. data-driven approach) i stałej aktualizacji. Jego ważnym składnikiem mogą być wyniki i parametry próbnych obciążeń, które stanowią punkt odniesienia w przyszłych ekspertyzach i kontrolach konkretnych obiektów.

The paper presents the concept of building a new system for managing bridge structures in Poland. It can be an extremely valuable tool supporting decision-making processes regarding specific bridges – for example regarding the need for their renovation, strengthening, introduction of SHM monitoring or demolition. This system is to be based on the philosophy of data-driven approach and constant updating. Its important component may be the load test results and parameters, which constitute a reference point for future expert opinions and inspections of specific structures. Currently, these data are archived in an uncoordinated and non-standardized manner. The authors’ proposal is supported by examples using neural networks and assumes the digital BIM aggregation of relevant data in the form of a website with an application programming interface (API).

A proposal to facilitate mandatory bridge load tests with artificial neural network analyses using a digital data aggregation platform

Owerko Piotr, Kałuża Jerzy, Wazowski Marek

Architecture Civil Engineering Environment

2021

Vol. 14, no. 3

69--78

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.