Improvement in Performance Prediction of Corroding Concrete Structures using Health Monitoring Systems

Abstract

Predicting future condition and reliability of the deteriorating structures is vital for their effective management. Probabilistic models have been developed to estimate and predict the extent of deterioration in concrete structures but their input parameters are fraught with uncertainties, hence limiting the effective use of the models for long term predictions. On the other hand, continuous innovations in the sensing and measurement technology have lead to the development of monitoring instruments that can provide continuous (or almost continuous) real time information regarding structural performance. Thus, powerful decisionsupport tools may be developed by combining information obtained through structural health monitoring with probabilistic performance prediction models. The potential benefits of improving performance prediction using health monitoring systems and their implications on the management of deterioration prone structures are presented in this paper. A typical structural element of a bridge (eg slab, beam or a cross beam etc) subjected to chloride induced deterioration is considered. It is shown that the confidence in predicted performance can be improved considerably through the use of health monitoring methods and hence, the management activities such as inspections, repair and maintenance etc can be adjusted whilst keeping consistent target performance levels. A comparison of various probabilistic models for the input parameters (eg exposure conditions, threshold chloride concentration etc) indicates that the effects of uncertainty can be minimised through the inservice health monitoring systems.