Structural Damage Detection Based On Cloud Computing

The related theories and operating techniques of structural health monitoring (SHM)and structural damage detection (SDD) become more skilled. However, most of thedetected methods are time-consuming and costly when the actual large complexstructures are assessed. Cloud computing (CC) is introduced to save the time-comsumingand increase efficiency.Traditional SDD methods are reviewed in this paper, focusing on an overview of thetime series analysis. Then the CC is introduced into the SHM field by stating itsdefinition, application, MapReduce programming model as its core technology, andHadoop as its open source. We separate the traditional time series analysis method intoMap and Reduce function module, combine with Hadoop, and propose a new CC-basedtime series analysis method for SDD by defining damage sensitive feature (DSF). Basedon the numerical simulations for single and multiple damages of a two-story rigid frame,we find that the new CC-based method can locate the structural damage accurately andidentify the severity of damages precisely. Moreover, the new CC-based method can savethe time-consuming. The more damage cases, the greater the speedup. Then we use theexperimental study on damage detection of a three-story building model from LosAlamos National Laboratory to verify the feasibility, effectiveness and accuracy of thenew method. So we can draw a conclusion that the new CC-based method proposed herecan locate the structural damage accurately and identify the severity of damages precisely.Further, the new CC-based method can save the time-consuming and increase efficiency.