A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks
Structural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridges is the effects of temperature variations on the measurement data, which can produce bigger effects in the response than the damage itself. In this study, a sensor-clustering-based time-series analysis method integrated with artificial neural networks (ANNs) was employed for damage detection under temperature variations. The damage features obtained solely using the time-series-based damage-detection algorithm are very effective for damage assessment; however, they yield false positives and negatives when temperature variations are present. Therefore, ANNs were used to compensate the temperature effects on the damage features obtained from time-series analysis. This methodology is applied to a footbridge finite-element model in which 2,000 simulations with temperature effects and damage cases were conducted. Results reveal that the proposed method can successfully determine the existence, location, and relative severity of damage using output-only vibration and temperature data even when temperature variations are present.
Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks
Structural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridges is the effects of temperature variations on the measurement data, which can produce bigger effects in the response than the damage itself. In this study, a sensor-clustering-based time-series analysis method integrated with artificial neural networks (ANNs) was employed for damage detection under temperature variations. The damage features obtained solely using the time-series-based damage-detection algorithm are very effective for damage assessment; however, they yield false positives and negatives when temperature variations are present. Therefore, ANNs were used to compensate the temperature effects on the damage features obtained from time-series analysis. This methodology is applied to a footbridge finite-element model in which 2,000 simulations with temperature effects and damage cases were conducted. Results reveal that the proposed method can successfully determine the existence, location, and relative severity of damage using output-only vibration and temperature data even when temperature variations are present.
Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks
Kostić, Branislav (author) / Gül, Mustafa (author)
2017-07-21
Article (Journal)
Electronic Resource
Unknown
| British Library Online Contents | 2017
Damage detection under varying temperature using artificial neural networks
| Wiley | 2017
VIBRATION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS
| Online Contents | 2013