Experimental Study on a Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization

Reinforced concrete (RC) durability is a crucial feature to estimate long-term quality and structural performance. The degradation model is vital for the resource planning of maintenance projects. This model will extract data by updating the status of structures and trending the components' state over time in terms of durability. Surface erosion, spalling, cracks, and other defects exposed on RC components lead to increase factors adversely affecting concrete durability in structures. This research presents an approach based on automated visualization for extracting quantitative indexes as well as visual inspection without the subjective interspersion of humans or probable human errors during the inspection. The durability index (D-i) will extract according to damage probability and defects growth in order to extract the severity of failure and risk. Measurement operation by automated software has been double-checked by manual measurement tools, and data will verify randomly in this method. The results show that, in this component, the damaged area increases by 24% after a definite time. According to degradation models, this component may pass the relative thresholds for the limit for the state of operations to fail. This significant difference between expected time and designing time determines the D-i, equal to 5 out of 10.

Automated summary

The durability of reinforced concrete is crucial for assessing long-term quality and structural performance. This research proposes an automated visualization approach for evaluating concrete durability by extracting quantitative indexes and conducting visual inspections, eliminating subjective interference and potential human errors.