| 1. |
|
|
| 2. |
- Chalouhi, Elisa Khouri, et al.
(författare)
-
Vibration-Based SHM of Railway Bridges Using Machine Learning : The Influence of Temperature on the Health Prediction
- 2018
-
Ingår i: Experimental Vibration Analysis for Civil Structures. - Cham : Springer Nature. - 9783319674438 - 9783319674421 ; , s. 200-211
-
Konferensbidrag (refereegranskat)abstract
- Civil engineering structures continuously undergo environmental conditions changes that can lead to temporary variations of their dynamic characteristics. Therefore, damage detection techniques have to be able to distinguish abnormal changes in the response due to damage from those normally related to environmental conditions variability. This paper addresses this issue by presenting a damage detection method that uses machine learning to detect and localize damage in railway bridges under varying environmental conditions (i.e. temperature). Results of the application to simulated data are shown with validation purposes. The first stage of the proposed algorithm consists in training a set of Artificial Neural Networks (ANNs) to predict deck accelerations during train passages assuming the bridge to be undamaged (or in a known state of preservation). In the second stage, the currently measured response is compared with that predicted by the trained ANNs. Since possible changes in the bridge state of preservation (damage) decrease the predictive accuracy of the ANNs, this comparison allows for the damage detection. During both stages, air temperature is given as input to the networks together with the train characteristics (i.e. speed and load per axle). The application results in the paper prove the ability of the algorithm to detect and localize damage. Furthermore, when the same procedure was applied neglecting the environmental factor, a noticeable decrease of the prediction power was met. This proves that changes in structural properties due to temperature variation can mask the damage occurrence and prevent its detection. The importance of accounting for environmental variations in damage detection is thus highlighted.
|
|
| 3. |
- Costa, Giancarlo, et al.
(författare)
-
Forecasting the Value of Vibration-Based Monitoring Information in Structural Integrity Management
- 2023
-
Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2557 .- 2366-2565. - 9783031391163 ; 433 LNCE, s. 21-31
-
Konferensbidrag (refereegranskat)abstract
- Structural deterioration and increasing load demand are two main factors that compromise the serviceability and functioning of civil constructions. The vastity of the bridge portfolio and the few resources available require maintenance optimization to provide the required user safety. In this context, vibration-based monitoring may provide information about the structural performance and support decisions in structural integrity management. In this paper, a novel definition of global and local information from a multi-sensor vibration-based system is provided and implemented for the cases of a parallel ductile Daniels system and a serial system. Furthermore, local and global integrity management actions are modeled and analyzed. Vibration-based information is used to optimize the maintenance strategy in terms of optimal action implementation. Decision and value of predicted information analyses are used to drive maintenance optimization. Indeed, each outcome of the monitoring system and maintenance strategy is associated with an expected utility and cost. Optimization is performed by determining the lowest expected cost corresponding to a maintenance strategy.
|
|
| 4. |
- Figueiredo, Eloi, et al.
(författare)
-
Does Climate Change Impact Long-Term Damage Detection in Bridges?
- 2023
-
Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2557 .- 2366-2565. - 9783031391163 ; 433 LNCE, s. 432-440
-
Konferensbidrag (refereegranskat)abstract
- The effects of operational and environmental variability have been posed as one of the biggest challenges to transit structural health monitoring (SHM) from research to practice. To deal with that, machine learning algorithms have been proposed to learn from experience based on a reference data set. These machine learning algorithms work well based on the premise that the basis of the reference data does not change over time. Meanwhile, climate change has been posed as one of the biggest concerns for the health of bridges. Although the uncertainty associated with the magnitude of the change is large, the fact that our climate is changing is unequivocal. Therefore, it is expected that climate change can be another source of environmental variability, especially the temperature. So, what happens if the mean temperature changes over time? Will it significantly affect the dynamics of bridges? Will the reference data set used for the training algorithms become outdated? Are machine learning algorithms robust enough to deal with those changes? This paper summarizes a preliminary study about the impact of climate change on the long-term damage detection performance of classifiers rooted in machine learning algorithms trained with one-year data from the Z-24 Bridge in Switzerland. The performance will be tested for three climate change scenarios in three future periods centered in 2035, 2060, and 2085.
|
|
| 5. |
- Giordano, Pier Francesco, et al.
(författare)
-
Value of Seismic Structural Health Monitoring Information for Management of Civil Structures Under Different Prior Knowledge Scenarios
- 2023
-
Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2557 .- 2366-2565. - 9783031391163 ; 433 LNCE, s. 11-20
-
Konferensbidrag (refereegranskat)abstract
- Seismic Structural Health Monitoring (S2HM) provides information about the integrity of civil structures and infrastructure in the aftermath of an earthquake. However, quantifying the benefits of S2HM information is crucial to justify the investment in S2HM systems. The benefit of S2HM can be computed through the Value of Information (VoI) from Bayesian decision theory, which compares the expected costs of alternative actions with prior information (without S2HM information) and with S2HM information (before it is available). This paper aims to analyze the VoI from S2HM in civil structures and infrastructure, considering different prior information scenarios regarding seismic action. The theoretical framework of the VoI is adapted to address three prior knowledge scenarios: (i) full information about the earthquake is available (ii) the intensity measure of the seismic motion is obtained using ground motion models, and (iii) no information is available. A numerical case study of a structure in a seismic area is presented, and the effect of different prior information scenarios on the VoI is discussed. The results show that VoI is higher when the prior information is low, indicating that monitoring systems are more valuable when uncertainty about seismic actions is high.
|
|
