TIS condition monitoring using ANN integration: an overview

Purpose: The purpose of this paper is to provide an overview of condition monitoring using artificial neural network (ANN) integration as a part of transportation infrastructure systems. Design/methodology/approach: This paper will review the concept of ANNs and its core functions for the optimization (to manage the asset in such a way that the condition does not fall below an acceptable minimum condition) of transportation infrastructure systems, in particular, the maintenance processes. In doing so, a specific and factual example of performance and condition measurement for roads will be also instigated. Findings: This paper demonstrated that ANN has many advantages if the problems cannot be solved by the clear algorithm. In addition, ANN has the ability to be instructed to handle large data set. There are various intelligent algorithms available and accordingly ANN is not a new concept. However, the ANN’s overall ability to solve complex and interchangeable system problems (such as one, which is found within the transportation infrastructure systems) is its core advantage. Originality/value: Although condition monitoring using ANN integration has been researched extensively, this paper provides additional example of integrated ANN for transportation infrastructure systems.