Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Areas vulnerable to natural disasters and damage estimation of infrastructure in Busan, Korea

Since the damages caused by disasters associated with climate anomalies and the diversification of the social structure increase every year, an efficient management system associated with a damage assessment of the areas vulnerable to disasters is demanded to prevent or mitigate the damages to infrastructure. The areas vulnerable to disasters in Busan, located at southeastern part of Korea, were estimated based on historical records of damages and a risk assessment of the infrastructure was performed to provide fundamental information prior to the establishment of the real-time monitoring system for infrastructure and establish disaster management system. The results are illustrated by using geographical information system(GIS) and provide the importance of the roadmap for comprehensive and specific strategy to manage natural disasters.

Damage quantitative assessment of spacecraft in a large-size inspection

To ensure the safety and reliability of spacecraft during multiple space missions,it is necessary to conduct in-situ nondestructive detection of the spacecraft to judge the damage caused by the hypervelocity impact of micrometeoroids and orbital debris(MMOD).In this paper,we propose an innovative quantitative assessment method based on damage reconstructed image mosaic technology.First,a Gaussian mixture model clustering algorithm is applied to extract images that highlight damage characteristics.Then,a mosaicking scheme based on the ORB feature extraction algorithm and an improved M-estimator SAmple Consensus(MSAC)algorithm with an adaptive threshold selection method is proposed which can create large-scale mosaicked images for damage detection.Eventually,to create the mosaicked images,the damage characteristic regions are segmented and extracted.The location of the damage area is determined and the degree of damage is judged by calculating the centroid position and the perimeter quantitative parameters.The efficiency and applicability of the proposed method are verified by the experimental results.