Center for Sustainable Engineering of Geological and Infrastructure Materials

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Infrastructure Materials

The United States is facing the downfall of progressively aging infrastructures that need rehabilitation. The latest America’s infrastructure report card evaluated the overall condition of the nation’s infrastructure with a very unsatisfactory grade D+ and reported a troubling increase over time of the deterioration rate of most infrastructures. Long-term aging and deterioration of structures also pose a significant threat to structural resiliency in the event of natural and man-made hazards. Recent structural collapses in the United States have shown that current design practice is inadequate to face the ever-broadening range of threats to public structures and government facilities, and structural retrofitting guidelines are unable to estimate the actual load carrying capacity of structures several years after their construction.

The prediction of the service lifetime of infrastructures requires the accurate prediction of the evolution of the probability of failure with time. Recent studies have shown that the probability of failure of infrastructure systems increases with time with either continuous or discrete increments. Continuous increments often result from a gradual deterioration of the system properties due to phenomena including, but not limited to, steel corrosion, freeze-thaw cycles, alkali silica reaction (ASR), delayed ettringite formation (DEF), creep, shrinkage, and early-age phenomena. Discrete increments can be due to shocks that cause sudden changes in the system properties and these include loads and deterioration mechanisms that are active for a short duration of time such as impact loads, seismic events, and vehicle impact.


The first scenario (red lines) is relevant to a situation in which an increase of the probability of failure beyond a level requiring retrofitting is not followed but any action, leading to the end of the service life of the structure at a certain point in time shortly after – especially if unexpected events occur. The second scenario (blue lines), on the contrary, shows a situation in which the probability of failure is reduced through appropriate structural retrofitting. As a consequence the service lifetime of the structure is increased, but the magnitude of such increase is directly related to reduction of probability of failure, which, in turn, depends on the extent, and consequently the cost, of the performed retrofitting. Multiple retrofitting scenarios can be of course envisioned during the service lifetime of the structure with the understanding, however, that subsequent repairs do require increasing resources for achieving the same reduction in the probability of failure.

The current societal demand for “sustainability” calls for the optimal management of infrastructures, which can be achieved only by accounting for numerous conflicting requirements associated with, for examples, economic costs, environmental impact, safety, and aesthetics, etc. For this reason, owners necessitate of a quantitative characterization of all these requirements so that they can find an optimal solution to their management and maintenance goals.

At any given time, there are two aspects that contribute to the estimation of the remaining service life of a structure: (1) the assessment of the current probability of failure, and (2) the prediction of the evolution of the probability of failure in the future and, in particular, the prediction of the time needed for the structure to reach some predetermined values of the probability of failure associated with the need of retrofitting or replacement. The latter corresponds to the service life of the structure.

While, in the current practice, the combination of current destructive and nondestrutive evaluation technologies provide a robust procedure for the assessment of the status quo from a qualitatively point of view, a quantitative accurate assessment of the current probability of failure is still hampered by the lack of a comprehensive framework able to link destructive and nondestructive measurements to the fundamental deterioration and failure mechanisms of the material. This is even more an issue when the evaluation is solely based on nondestructive evaluation as required by the fact that destructive evaluations tend to be too invasive and too expensive to be performed on a regular basis.

The overarching goal of our center research program is to develop the missing computational technologies that will enable the calculation of the probability of failure (and the remaining service life) of infrastructures on the basis of NDE measurements. A Virtual Concrete Infrastructure Simulator will be built to interpret NDE data based on nonlinear wave mixing technologies through the use of accurate meso-mechanical models for concrete aging and deterioration. Structural multiscale computational analysis will be performed to predict the evolution of the capacity of aged/deteriorated infrastructures. Ultimately, this will allow predicting the evolution of the probability of failure when introduced in a structural reliability framework equipped with accurate demand models. The vision is to ultimately be able to perform (real-time) NDE-enabled model updating to continuously improve the prediction of the service life of infrastructures.