Recent advancement of concrete dam health monitoring technology: A systematic literature review

Abstract

The health monitoring of dams has become a topic of paramount importance to maintain the sustainability and resiliency of our society. The dam health monitoring (DHM) models provide an estimate of dam responses such as crest displacement, concrete stress etc., which can be compared with the actual measurements to draw conclusions about dam safety. This paper reviews various models that have been developed in the past few years for DHM. In this paper, the existing models have been classified into three categories namely, numerical, data-driven, and hybrid models. The numerical models are the most accurate and can be applied even for the first filling of the reservoir where no monitoring data is available. On the other hand, the data-driven model is a good choice in case if a dam has been instrumented and long-term data has been collected. Finally, the hybrid model is most suited if limited monitoring data is available and the mechanism of dam deterioration is partially known. Among these three models, the data driven models have gained significant attention in recent years due to advancements in artificial intelligence and other statistical tools. In this paper, a systematic review of these models has been presented along with a brief mathematical details, and their application to an existing dam. The novelty of the paper is that it covers a broad range of the developed models including their applications, advantages and limitations. In summary, this paper summarizes the existing knowledge in the field of DHM and provides an opportunity to dam owners and practitioners to select the best model that can be implemented for an existing dam.