Advances in Early-stage Novelty Inspection and Structural Condition Assessment
1Xi’an Jiaotong University, Xi’an, China
2Universidade Lusófona, Lisbon, Portugal
3East China Jiaotong University, Nancang, China
4Tongji University, Shanghai, China
5Southwest Petroleum University, Chengdu, China
Advances in Early-stage Novelty Inspection and Structural Condition Assessment
Description
In civil and mechanical engineering, periodic inspection for novelty is essential for certain structural condition assessment and further maintenance. Structural condition degrades during long-term, in-service in engineering, and under such circumstances, new defects and unknown novelties emerge and expand, which could potentially induce structural failures.
The early-stage novelty inspection, one essential step in long-term structural health monitoring, is still challenging since the ambient conditions also induce too much uncertainty. For instance, in terms of long-span suspension bridges or cable-stayed bridges, the effects of wind and temperature are of high significance and have to be considered before construction and given attention during their in-service lives.
This Special Issue aims to summarize the recent progress in early-stage novelty inspection techniques, structural condition assessment, and damage identification. Applications in engineering, including both theoretical and experimental studies, are encouraged. This Special Issue welcomes studies related to novelty inspection and structural condition assessment from sensor placement optimization, signal processing, field testing, structural optimization, etc. In addition, reviews summarizing advances over recent years are also welcome.
Potential topics include but are not limited to the following:
- Novelty inspection techniques
- Structural condition assessment
- Sensor placement optimization
- Advances in structural dynamics analysis
- Long-term structural condition assessment
- Temperature effect filtering approaches
- Transmissibility based techniques
- Noise filtering approaches
- Artificial intelligence in structural dynamics
- Big data processing in damage identification