A Structural Impairment Detection System Using Competitive Arrays of Artificial Neural Networks

Impairments that occur in structural systems may degrade performance or prevent a structure from functioning safely. Detecting impairments prior to a structural failure reduces the effect of impairments on the safety of those using a structure. This article describes the computational framework of a Structural Impairment Detection System (SIDS) that processes the digital data streams of electronic sensors attached to critical components of a structure. The resulting system comprises a competitive array of neural networks that can accurately describe the types and severity of likely impairments present in the structure. The competitive array of neural networks is trained to detect patterns in data streams specific to likely target impairments. Data streams generated from ABAQUS models and from electronic sensors are used in training and evaluating the system. As part of the initial development of this SIDS, the counterweight truss of a 100-year old railroad drawbridge was instrumented and evaluated. The resulting data streams were diagnosed autonomously by the SIDS as being similar to one of two operational conditions: unimpaired or a single impairment present in a member embedded within the counterweight. Further investigation of the counterweight truss is underway to verify the accuracy of the assessments.