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Digital Communications and Networks








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.


This work is jointly sponsored by the National Science Foundation (NSF) with proposal number 1240734 and the UTC THEC/CEACSE 2016 Grant Program.

Open Access funded by Chongquing University of Posts and Telecommunications, published under an Attribution-Noncommercial-NoDerivatives 4.0 International (CC BY-NC-ND-4.0) license (

Original Publication Citation

Liang, Y., Wu, D., Liu, G., Li, Y., Gao, C., Ma, Z. J., & Wu, W. (2016). Big data-enabled multiscale serviceability analysis for aging bridges. Digital Communications and Networks, 2(3), 97-107. doi: 10.1016/j.dcan.2016.05.002


0000-0003-0178-1876 (Yaohang Li), 0000-0002-3019-1886 (Cuilan Gao), 0000-0001-8246-7605 (Zhongguo John Ma)