Abstract
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.
Highlights
With the sustained economic development, the traffic volume the overloading vehicles increased rapidly.is seriously threats the structural safety of bridges those constructed as early as the 19th∼the mid-20th century whose extended service lives have caused dangerous accumulation of structural damages in long-term use [1,2,3]
Even though the structural health monitoring information is available, they still make decisions based on their common sense and prior experience instead of actions suggested by the Structural health monitoring (SHM) system. e embarrassing situation results from a principle which goes beyond the scope of the bridge managers
To address this research gap, this paper aims to develop a computing model for quantitatively assessing the value of structural monitoring information based on Bayesian theory. rough scenario simulation and sensitivity analysis based on the developed model, the main influencing factor of value of structural health monitoring information is identified and discussed
Summary
With the sustained economic development, the traffic volume the overloading vehicles increased rapidly. A completed SHM system usually consists of 6 modules including sensory system, data acquisition and transmission system, data processing and control system, structural health evaluation system, structural health data management system, and inspection and maintenance system [6,7,8]. It can provide realtime information about structural conditions of bridges (environmental loads and status, operation loads, bridge features, and structural responses) by integrating various state-of-art technologies such as sensory technology, deep learning, big data, and machine vision [9, 10]. Even though the structural health monitoring information is available, they still make decisions based on their common sense and prior experience instead of actions suggested by the SHM system. Is study can deepen managers’ understanding of value of SHM and eliminate their doubt on reliability of SHM
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