Cloud Computing-Based Time Series Analysis for Structural Damage Detection

Abstract

The structural damage detection (SDD) and on-line integrity assessment is one of the fundamental objectives in the field of structural health monitoring (SHM). Unfortunately, some of the existed SDD methodologies are time-consuming, and more efficient methods are needed as some large complex structures are assessed in practice. In this study, the cloud computing (CC) technology is introduced into the SDD domain for saving the computation cost and for improving the computation efficiency. The CC definition and application are introduced. Then MapReduce is described as its core technology and Hadoop platform adopted as its open source as well. The traditional time series analysis is separated into map and reduce function modules. In combination with the Hadoop, a new CC-based time series analysis method is proposed for SDD by combining CC technology with time series analysis and further by defining damage-sensitive feature (DSF). The performance and efficiency of the proposed CC-based SDD method is assessed by some numerical simulations for single and multiple damages of a two-story rigid frame, further by a series experimental data downloaded from the web site of the Los Alamos National Laboratory (LANL), United States, for damage detection of a three-story building model under the linear and nonlinear damage conditions of structures. The illustrated results show that the new CC-based SDD method can effectively locate the structural damage and quantify the damage severity to some extent. It can save the computation cost and enhance the computing efficiency of SDD implementation. The more the damage cases are, the more significant the speedup ratio is. It is more beneficial to the analysis and processing of the mega data measured on site in the SHM field.