Integrating the artificial intelligence techniques into Bridge Information Modeling (BrIM)

While the Bridge Management System (BMS) has a crucial role in bridge performance, Bridge Information Modeling (BrIM) has been introduced recently to enhance the proceedings of the whole phases of bridge life-cycle starting with concept and design, through construction and operation, and ending with maintenance and rehabilitation. Different software applications have been developed and commercially spread to implement these tools and to help decision makers in their tasks in order to visualize their choices. Available software focus on geometric implementations and cost analyses which are the main factors to be verified. In order to benefit from previously constructed bridge projects and leverage the historical knowledge and information they provide, this paper proposes a methodology to introduce bridge success and failure performances. Bridge elements and components have to be defined to cover the whole bridge types by establishing appropriate libraries stored in a database. The Data will be customized based on the available information related to bridge information resources. Afterward, an engine based on "machine technique", -a branch of Artificial Intelligence (AI)-using Artificial Neural Network (ANN) modeling with its back-propagation algorithm will be included part of the proposed methodology to identify and select the utmost solution based on the restricted factors like the Benefit/Cost value. An iterative process is considered to attend the desired and balanced results. The process will be automatic and will require minimal user intervention. The proposed methodology will help the decision makers (engineers and management's agencies) to visualize their decision benefit based on previous projects performances for the good of society.