Multi-Span Steel–Concrete Bridges With Anti-seismic Devices: A Case Study

Pucinotti, Raffaele; Fiordaliso, Giovanni

doi:10.3389/fbuil.2019.00072

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…A simplified Finite Element Model (FEM) using one-dimensional elements (as beams, truss, and rigid links) with properties equivalent to those of the real elements, was then used to obtain the infrastructure response to the stresses of the loads previously highlighted [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Barrile

Fotia

Bilotta

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Road infrastructures systems are critical in many regions of Italy, counting thousands of bridges and viaducts that were built over several decades. A monitoring system is therefore necessary to monitor the health of these bridges and to indicate whether they need maintenance. Different parameters affect the health of an infrastructure, but it would be very difficult to install a network of sensors of various kinds on each viaduct. For this purpose, we want to finalize the use of geomatics technologies to monitor infrastructures for early warning issues and introducing automations in the data acquisition and processing phases. This study describes an experimental sensor network system, based on long term monitoring in real-time while an adaptive neuro-fuzzy system is used to predict the deformations of GPS-bridge monitoring points. The proposed system integrates different data (used to describe the various behaviour scenarios on the structural model), and then it reworks them through machine learning techniques, in order to train the network so that, once only the monitored parameters (displacements) have been entered as input data, it can return an alert parameter. So, the purpose is to develop a real-time risk predictive system that can replicate various scenarios and capable to alert, in case of imminent hazards. The experimentation conducted in relation to the possibility of transmitting an alert parameter in real time (transmitted through the help of an experimental control unit) obtained by predicting the behavior of the structure using only displacement data during monitoring is particularly interesting.

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Section: Resultsmentioning

confidence: 99%

Road Infrastructure Monitoring: An Experimental Geomatic Integrated System

Barrile

Fotia

Bilotta

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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“…During the design of a construction, the (static or dynamic) actions are known and the structural model is possessed, this knowledge is combined to obtain the prediction of the structural response in the various conditions of interest (state of interest service limit and last limit status). Surveys and tests (in situ or in the laboratory) that provide the structural response to certain stresses are carried out in the monitoring of an existing work [20]. In this case then, we are aware of the response and actions, while what we want to determine is the model.…”

Section: Structural Modelmentioning

confidence: 99%

“…The spans of the viaduct, built in the 70s, in concrete of class C20/25, with a cubic resistance characteristic of 25 MPa, have a length of 28 m. The scheme of its cross-section is shown in Figure 5b. Moreover, the FE model was validated through an output-only ambient vibration test described in [20,24]. Generally, a static load test is performed to verify the actual behavior of a structure, while a dynamic test is performed in order to validate the FE model for the structure.…”

Section: Structural Modelmentioning

confidence: 99%

“…Therefore, model updating was used to minimize the 'difference' between FEA and reference test data. In the model updating, the procedure presented in [20] was performed.…”

Section: Structural Modelmentioning

confidence: 99%

“…The parameters chosen for model updating were [20,22,24]: Simultaneously, we decided to insert in the FE model the experimental elastic modules of the actual materials (steel and concrete). Table 1 shows the comparison between experimental frequencies and numerical frequencies obtained by using a commercial program.…”

Section: Structural Modelmentioning

confidence: 99%

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Geomatics and Soft Computing Techniques for Infrastructural Monitoring

et al. 2020

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Structural Health Monitoring (SHM) allows us to have information about the structure under investigation and thus to create analytical models for the assessment of its state or structural behavior. Exceeded a predetermined danger threshold, the possibility of an early warning would allow us, on the one hand, to suspend risky activities and, on the other, to reduce maintenance costs. The system proposed in this paper represents an integration of multiple traditional systems that integrate data of a different nature (used in the preventive phase to define the various behavior scenarios on the structural model), and then reworking them through machine learning techniques, in order to obtain values to compare with limit thresholds. The risk level depends on several variables, specifically, the paper wants to evaluate the possibility of predicting the structure behavior monitoring only displacement data, transmitted through an experimental transmission control unit. In order to monitor and to make our cities more "sustainable", the paper describes some tests on road infrastructure, in this contest through the combination of geomatics techniques and soft computing.

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