Applying KDD to a Structure Health Monitoring System Based on a Real Sited Bridge: Model Reshaping Case

2014 ◽  
Vol 472 ◽  
pp. 535-538 ◽  
Author(s):  
Ming Lei Ma ◽  
Gui Ling Wang ◽  
Dong Mei Miao ◽  
Gui Jun Xian
Keyword(s):  
Data Mining ◽  
Structural Health Monitoring ◽  
Knowledge Discovery ◽  
Health Monitoring ◽  
Bridge Engineering ◽  
Massive Data ◽  
Time Data ◽  
Health Monitoring System ◽  
Bridge Model ◽  
Real Time Data

Knowledge discovery (KDD) method aims to solve the problem of massive data. For bridge engineering, the structural health monitoring (SHM) system is cumulative data from time to time, but the whole system should be understudied in real time. Data mining should be used in one of the KDD process. This article proposed a regular rule of analyzing the SHM data from a real sited bridge. The data aim to help engineers understanding the system degradation of the bridge.

scholarly journals Real-time data mining of massive data streams from synoptic sky surveys

2016 ◽  
Vol 59 ◽  
pp. 95-104 ◽  
Author(s):  
S.G. Djorgovski ◽  
M.J. Graham ◽  
C. Donalek ◽  
A.A. Mahabal ◽  
A.J. Drake ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Data Streams ◽  
Massive Data ◽  
Time Data ◽  
Sky Surveys ◽  
Real Time Data

scholarly journals A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Baoquan Cheng ◽  
Lijie Wang ◽  
Jianling Huang ◽  
Xu Shi ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Behavior Pattern ◽  
Bridge Engineering ◽  
Health Monitoring System ◽  
Structural Health ◽  
Computing Model ◽  
Structural Health Monitoring System ◽  
Monitoring Information

Structural health monitoring system can provide valuable information for improving decision-making process in maintenance and management of bridges. However, managers usually lack understanding of value of structural health monitoring information. This paper developed a computing model for quantifying the value of structural health monitoring information based on Bayesian theory. Then, the model was demonstrated and validated using a simple case and the key factors (i.e., system accuracy, reparation cost, prior probability of structural failure, and manager’s behavior pattern) influencing the value of structural health monitoring information were identified and discussed. Findings from this study help to answer the question of whether a structural health monitoring system should be installed and run, thus enriching the knowledge body of structural health monitoring.

The Structural Health Monitoring System of the Governor Mario M. Cuomo Bridge

2018 ◽  
Author(s):  
Isaac Farreras-Alcover ◽  
Jacob Egede Andersen ◽  
Preston Vineyard
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Time Data ◽  
Health Monitoring System ◽  
Operational Conditions ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
The Usa ◽  
Structural Responses

The Governor Mario M. Cuomo Bridge, also known as the New NY Bridge is a twin cable-stayed bridge that replaces the Tappan Zee Bridge, in the USA. The bridge is equipped with a Structural Health Monitoring System (SHMS) consisting of more than 400 sensors deployed at relevant locations. The sensors capture environmental and operational conditions as well as the associated structural responses. The system is designed to process monitoring data to support data-driven management of the bridge. This is achieved through the system''s different functionalities, which include real-time data visualization via an on-line graphical user interface, customized data processing routines, alert notifications whenever data-based thresholds are exceeded, automatic reporting of pre-defined parameters and characterization of structural responses during extreme events. The present paper describes, as a case-study, the motivation, architecture, functionalities and installation aspects of the SHMS.

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