Monitoring and data‐informed approaches for the assessment of existing structures

Abstract

AbstractAccurate information from monitoring of structures plays a critical role in making informed decisions regarding structural safety control and maintenance needs. Unfortunately, there are several gaps in the existing standards and the monitoring practices at local, national, and transnational (e.g., European) levels. The wide diversity of assets and their respective environments further complicates the development of standardized monitoring approaches. Despite the constantly increasing number of examples and intensive knowledge exchange on the use of information from inspection, testing, and monitoring to support the safety and risk assessment of existing structures, major challenges are being faced in establishing consensus on standardization in this domain. At the same time, the need for robust standardization that enables integrating structural monitoring with the assessment of existing structures is generally acknowledged by public authorities and industrial stakeholders. Extending the lifespan of structures to enhance sustainability is a critical imperative of our time and monitoring is expected to be key element of reliable service life extension of existing structures. However, development and adoption of new standards involve complex processes that require multi‐level dialogues, consensus, and commitment from political and industrial stakeholders to move forward from legacy procedures. The fib Model Code 2020 (MC2020) serves as a significant milestone in the development of new standards for monitoring and maintenance of concrete structures. It addresses the existing gaps in knowledge and seeks to close the gap between the practice and the standards with regard to the monitoring of structures. The important objectives of the MC2020 are to support the standardization bodies in formulating new standards in monitoring, maintenance, and safety of existing concrete structures. This paper presents the fundamental/basis for shaping the methodology of data‐driven assessment and the methodologies proposed in the ModelCode.