Optimize the Data Collection for the Service-life Prediction of Concrete Port Infrastructure

Abstract: For Port Authorities, waterfront assets like quay walls, wharves and jetties are the pivot of their business cases. The Port’s income will depend on whether the asset is capable of performing adequately during the lease period of the tenant. When not looked properly after, the harsh conditions of the maritime environment will cause major deteriorations of the assets. Being able to predict when to intervene with maintenance is key for the asset manager
Starting in 2011, the Port of Rotterdam initiated fort thar reason the KMS program, which aimed to assist asset managers in their effort to optimize the prediction of needed maintenance of concrete waterfront structures. The software portion of the KMS program focused on connecting the port legacy system to service-life prediction model and risk calculation expert applications. At the hearth of the KMS, an advanced reactive transport model called STADIUM was used to simulate the exposure to brackish water and seawater to the port infrastructure and predict degradation mechanisms such as rebar corrosion.
To feed the service-life prediction model, information on the in-place concrete is essential. Accordingly, a specific inspection protocol was put in place to collect information relevant to the predictive model. Base information needed to perform service-life predictions includes concrete properties such as porosity and diffusion coefficient, as well as steel reinforcement cover thickness. This initial KMS inspection program relied heavily on coring and laboratory testing. Yearly inspections allowed gathering information on multiple quay wall sections built at different times and exposed to different environments, ranging from low salinity ones to the seawater of the North Sea.
The initial inspection procedure was first optimized by performing statistical analyses of the data gathered in the first six years of the KMS program. This allowed reducing coring operations by half. To refine the information collected on site, coring was replaced by the use of non-destructive tests, which not only helped to compensate the reduced number of cores but added new layers of information that actually greatly enhanced the service-life predictions of the different concrete structures. Non-destructive tests performed on site at this point consisted in corrosion potential and ground-penetrating radar measurements.
The third phase of the KMS inspection program is underway and aims at completely eliminating coring activities. Instead, service-life analysis will rely solely on a minimal amount of material collected on site and almost exclusively on non-destructive surface testing. To the tests cited earlier, surface resistivity measurements were added to the program as a mean to evaluate concrete properties without extracting cores.
The combination of the use of past data with on-site non-destructive testing actually generates more meaningful data that improve service-life predictions, compared to the initial coring program. This, in turn leads to a better estimation of the different quay walls service-life, which helps managers optimize structure maintenance budgets and extend the service-life of key assets. Perhaps more importantly, in the ever-present context of global warming, optimizing existing resources instead of building anew fits perfectly in a sustainable approach to human activities

How does this project / topic contribute to the advancement of the industry and profession?: At the time it was initiated, the KMS program was considered one of the most advanced and ambitious concrete waterfront structure asset management initiative. The unique combination of advanced modeling and optimized data collection procedures still represent today a leading-edge approach to marine structure asset management.

Does the project / topic implement new and innovative techniques, materials, technologies, and delivery methods?: The topic specifically presented here will focus on the use of non-destructive testing methods in the context of service-life predictions of concrete waterfront structures. The methods themselves are routinely used. However, their combination with historical data and a service-life prediction model is innovative.

What was the most challenging aspect of your project / topic and how did you handle it to ensure success?: The challenge was two-fold. First, the amount of data generated with the non-destructive surface tests is important and required new technology in order to be handled properly. For instance, the data collected using ground-penetrating radar required the development of an AI-based module to reduce analysis time. The second challenge consisted in mapping non-destructive surface test results to actual material properties. As the surface data are not, per se, material properties, their incorporation in an approach relying strongly on a predictive model required research work to obtain meaningful data out of those tests.

Who is the target audience for this paper?: (Port) waterfront (infra)structure asset managers and engineers involved in maintaining the readiness of such structures.