Advanced framework for degradation modeling of operating structures

Abstract

The safe operation of steel structures, such as bridges, is of paramount importance to mitigate potential issues. Consequently, the continuous and thorough monitoring of their operational conditions is imperative to uphold their safety and reliability. However, the inexorable process of corrosion, catalyzed by environmental conditions, leads to the inevitable deterioration of structural integrity over time. This research endeavors to predict the extent of corrosion in the primary cables of bridges through the application of advanced methodologies based on machine learning techniques. The execution of the proposed model necessitates the utilization of an extensive database encompassing diverse characteristics pertaining to the environmental properties of the surrounding region. The performance of the proposed models is rigorously assessed using a comprehensive suite of statistical and graphical metrics. The findings of this investigation underscore the effectiveness of the recommended solutions, surpassing previously established methodologies in addressing this pressing issue. The demonstrated success of the proposed model augurs favorably for its potential utility in furthering research into the dependability assessment of suspension bridge main cables.