Big data-enabled multiscale serviceability analysis for aging bridges☆

Digital Communications and Networks

Guirong Liu¹  Weidong Wu²  Zhongguo John Ma³  Yu Liang⁴  Dalei Wu⁴  Yaohang Li⁵  Cuilan Gao⁶ 

[1] Department of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USA;Department of Civil Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA;Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA;Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA;Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA;Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA;

关键词: Hadoop Ecosystem;    Bridge;    Serviceability;    Multi-scale;    Reliability analysis;    Deep learning;   

DOI  :  10.1016/j.dcan.2016.05.002

来源: DOAJ

【 摘 要 】

This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge serviceability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method (e.g., association rule learning) or structural mechanics modeling and simulation.

【 授权许可】

Unknown