No abstract

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

This paper is available online at www.jtaer.com DOI: 10.4067/S0718-1876201400020000

No abstract

This paper discusses the challenges of publishing Persian linked data based on an experience of publishing some academic data from Ferdowsi University of Mashhad dataset. By analyzing the experimental results of the project and classifying the problems, some publisher-oriented solutions are proposed to improve the quality of datasets on the web.