Multidimensional tensor strategy for the inverse analysis of in-service bridge based on SHM data

摘要

The inverse analysis and evaluation of in-service bridges by considering structural health monitoring (SHM) data, which is something of a black box problem, is usually affected by a number of uncertain factors such as the monitoring items, the monitoring cycles and the SHM data analysis methods. It is also generally accepted that the storage device and analysis software, such as computer hard disk and CPU, are often more demanding when dealing with large amounts of data. In addition, data quality issues such as excessive noise, poor periodicity and incomplete data are often encountered for analysis of large volumes of data. Relatively speaking, it has become the first choice of many researchers to intercept some partial data with good data quality as samples for analysis. For example, a sample of 1 day's data or a sample of 1 week's data is usually selected in traditional statistic analysis. However, the SHM data are not being used to its full potential and the sample data are often accompanied by a degree of subjectivity, randomness and fuzziness. More importantly, the deeper multidimensional characterisation and visualisation of the massive SHM data set itself is also in urgent need of development. This usually includes the sparse matrix characteristics of the SHM data set as well as the common correlation of different monitoring items. Therefore, it is necessary to carry out the analysis of SHM data as a whole. In this study, the multidimensional tensor analysis method from computational mathematics is applied, and then, a tensor analysis strategy is proposed for SHM data. As a case study, the in-service prefabricated slab-on-girder bridge is also presented. In particular, all the monitoring items and the whole monitoring cycle can be taken into account and visualised in a better way. A flow chart consisting of five stages is also provided, including the initial tensor construction, the tensor decomposition, the tensor prediction, the tensor reconstruction and the error analysis. The multidimensional tensor coupling is also a further development from the traditional correlation analysis of different types of monitoring data. It is then to be expected that this will further reflect the actual operational status of the bridge in-service. Some critical issues such as the rank value and the dynamic tensor model are also discussed. It is expected that the strategies proposed herein will be applied not only to the construction of smart cities, but also to big data in the industrial sector.