Mathematical Problems in Engineering

Structural Health Monitoring-Oriented Data Mining, Feature Extraction, and Condition Assessment


Publishing date
28 Feb 2014
Status
Published
Submission deadline
11 Oct 2013

Lead Editor

1School of Civil Engineering, Research Center for Structural Health Monitoring and Control, Dalian University of Technology, Dalian 116023, China

2Department of Civil Engineering, Geodesy and Geodetic Applications Laboratory, Patras University, Patras 26500, Greece

3Department of Civil Engineering, Zhejiang University, Anzhong Building, Zijingang Campus, China

4School of Civil and Resource Engineering, University of Western Australia, Crawley, WA, Australia


Structural Health Monitoring-Oriented Data Mining, Feature Extraction, and Condition Assessment

Description

Structural health monitoring (SHM) is the usage of modern sensing technologies, communication algorithms, and advanced data mining and management systems to monitor the operational environments and loadings as well as the structural responses in real time to effectively evaluate the structural health condition and safety performance and to instruct the daily structural inspection and maintenance, which is a multidiscipline and cutting-edge research field in civil engineering. In the past several decades, a great amount of long-term SHM systems have been designed and implemented worldwide on civil engineering structures such as large-scale bridges and high-rise buildings. With these SHM systems, a vast ocean of information relevant to the structural responses and behavior can be continuously obtained in real time. The measurement data are valuable in detecting structural anomalies and damage at an early stage to ensure operational safety and providing authentic information for timely assessment after disasters and extreme events.

A critical issue of great concern is how to extract the features inherent in the monitoring data for structural performance assessment targeting to life-cycle safety, reliability, durability, and sustainability. Therefore, intelligent computational methodologies and approaches such as artificial neural networks, fuzzy logic systems, and genetic algorithms are always desired. In this connection, we invite investigators to contribute original papers and review articles of the theme of SHM-oriented data mining, feature extraction, and condition assessment based on advanced mathematical methods. Potential topics include, but are not limited to:

  • Data-driven structural health evaluation based on wired or wireless system
  • Nature-inspired intelligent computational methods for data mining
  • Damage detection and system identification by the use of real-world monitoring data
  • Reliability-based structural condition/safety assessment with uncertainties
  • Development of novel analytical models for structural feature extraction
  • Other related

Before submission authors should carefully read over the journal’s Author Guidelines, which are located at http://www.hindawi.com/journals/mpe/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/mpe/shm/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2014
  • - Article ID 941780
  • - Editorial

Structural Health Monitoring-Oriented Data Mining, Feature Extraction, and Condition Assessment

Ting-Hua Yi | Stathis C. Stiros | ... | Jun Li
  • Special Issue
  • - Volume 2014
  • - Article ID 232763
  • - Research Article

A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

Shao-Fei Jiang | Si-Yao Wu | Li-Qiang Dong
  • Special Issue
  • - Volume 2014
  • - Article ID 185487
  • - Research Article

Complete Inverse Method Using Ant Colony Optimization Algorithm for Structural Parameters and Excitation Identification from Output Only Measurements

Jun Chen | Xin Chen | Wei Liu
  • Special Issue
  • - Volume 2014
  • - Article ID 369540
  • - Research Article

Innovative Data Fusion Enabled Structural Health Monitoring Approach

Xiukuan Zhao | Ruolin Wang | ... | Y. L. Mo
  • Special Issue
  • - Volume 2014
  • - Article ID 987694
  • - Research Article

Structural System Identification with Extended Kalman Filter and Orthogonal Decomposition of Excitation

Y. Ding | B. Y. Zhao | B. Wu
  • Special Issue
  • - Volume 2014
  • - Article ID 956473
  • - Review Article

A State-of-the-Art Review on Fatigue Life Assessment of Steel Bridges

X. W. Ye | Y. H. Su | J. P. Han
  • Special Issue
  • - Volume 2014
  • - Article ID 909682
  • - Research Article

PZT-Based Detection of Compactness of Concrete in Concrete Filled Steel Tube Using Time Reversal Method

Shi Yan | Jinzhi Fu | ... | Fuxue Liu
  • Special Issue
  • - Volume 2014
  • - Article ID 628619
  • - Research Article

Intelligent Platform for Model Updating in a Structural Health Monitoring System

Danhui Dan | Tong Yang | Jiongxin Gong
  • Special Issue
  • - Volume 2014
  • - Article ID 546478
  • - Research Article

A Method of Data Recovery Based on Compressive Sensing in Wireless Structural Health Monitoring

Sai Ji | Yajie Sun | Jian Shen
  • Special Issue
  • - Volume 2014
  • - Article ID 405760
  • - Research Article

Structural Damage Identification Based on the Minimum System Realization and Sensitivity Analysis

W. R. Li | Y. F. Du | ... | L. J. Zhao
Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
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Impact Factor1.305
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