This Faculty Early Career Development Program (CAREER) project is to explore, develop and promote an autonomous decision supporting system for the life-cycle corrosion management of steel structures and steel reinforced concrete structures. The innovative technology can transform the current practice of time-based visual inspection with inconsistent decisions to a sensor/data-based decision-supporting process for timely corrosion management. The intelligent system can not only inform end users identify and reduce safety risks to avoid unexpected failure and associated fatality but it can also achieve operational excellence by enhancing the service life of aging infrastructures where corrosion degradation is a main concern. As corrosion has shown to cost about 3.1 percent of the national gross domestic product, this mitigation/management technology can have significant cost saving, and thus greatly benefit national economy. The results and findings from this interdisciplinary research can be directly integrated into corrosion-related curriculums, attracting a broader participation of both undergraduate and graduate students from several disciplines. The pre-college, CORrosion Engineers (CORE), program with learning activities from video lectures on corrosion fundamentals to hands-on qualification tests of clothes as corrosion protection coating for minimum water penetration can stimulate the interests for STEM educations among K-12 young girls. In addition, two CORE programs are designed to involve middle and high school students from the underrepresented groups.
The system consists of a corrosion protective duplex coating of an inner thermally-sprayed non-ferrous metal layer and an outer nanomaterial modified polymeric sealing paint layer, a corrosion assessment strategy with distributed fiber optic sensors embedded in the duplex coating, and a decision-making tool with dynamic risk assessment modeling. This research will advance the duplex coating technology by 1) optimizing composites of non-ferrous metal coating to achieve robust mechanical and corrosion resistance, and 2) understanding the chemical interaction between nano-additives and polymeric paints to accomplish the desirable dispersion. The interface bonding mechanics and behavior of duplex coating with both steel substrates and fiber optic sensors, more importantly, the effect of metal composites and nano-additives on improving the bond strength will be investigated using pull-off tests, scanning electron microscopy (SEM) with energy dispersion system (EDS) for interface structure and state analysis, and X-ray diffraction (XRD) technique for phase composition analysis. With the distributed fiber optic sensors embedded in duplex coating layers, this research also aims to understand the in-situ corrosion deterioration mechanism of coated structural steel with combined cathodic and barrier protection. The in-situ corrosion data availed by the intelligent system enable the understanding on the path and rate of moisture and oxygen penetration through duplex coating to assess scenarios of the uniform corrosion through a large area of porous coatings or coating degradation, or pitted corrosion through local defects and damage. The corresponding assessment will be fed into a real-time corrosion condition updating algorithm to develop a self-updated hybrid corrosion risk management model for a cost-effective prioritization of mitigation actions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
