• Smart Structures and Systems
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• v.13 no.3
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• pp.407-418
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• 2014

The purpose of this paper is to present an Integrated Life Cycle Bridge Information Modeling that can be used throughout different phases of the bridge life cycle including: design, construction, and operation and maintenance phases. Bridge Information Modeling (BrIM) has become an effective tool in bridge engineering and construction. It has been used in obtaining accurate shop drawings, cost estimation, and visualization. In this paper, BrIM is used as an integrated tool for bridges life cycle information modeling. In the design phase, BrIM model can be used in obtaining optimum construction methods and performing structural advanced analysis. During construction phase, the model selects the appropriate locations for mobile cranes, monitors the status of precast components, and controls documents. Whereas, it acts as a tool for bridge management system in operation and maintenance phase. The paper provides a detailed description for each use of BrIM model in design, construction, and operation and maintenance phases of bridges. It is proven that BrIM is an effective tool for bridge management systems throughout their life phases.

• Smart Structures and Systems
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• v.26 no.4
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• pp.533-544
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• 2020

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.

• Smart Structures and Systems
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• v.17 no.4
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• pp.669-690
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• 2016

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.