Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification

• Published in: Applied sciences 2024-04, Vol.14 (8), p.3298
• Main Authors: Machorro-Lopez, Jose M., Yanez-Borjas, Jesus J., Valtierra-Rodriguez, Martin, Amezquita-Sanchez, Juan P.
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Bridges; Cables; damage identification; efficiency increment; Entropy; highway bridges; Identification; Localization; Methods; Natural disasters; Nonparametric statistics; vibration signals; wavelet; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14083298

Highway bridges are crucial civil constructions for the transport infrastructure, which require proper attention from the corresponding institutions of each country and constant financing for their adequate maintenance; this is important because different types of damage can be generated within these structures, caused by natural disasters, among other sources, and the heavy loads they transport every day. Therefore, the development of simple, efficient, and low-cost methods is of vital importance, allowing us to identify damage in a timely manner and avoid bridges collapsing. As reported in a previous work, the wavelet energy accumulation method (WEAM) and its corresponding application in the Rio Papaloapan Bridge (RPB) represented an important advance within the field. Despite identifying damage in bridges with precision and at a low cost, there are several aspects to improve in that method. Therefore, in this work, that method was improved, eliminating several steps, and meaningfully reducing the computational burden by implementing an algorithm based on the Shannon entropy, thus giving way to the new entropy wavelet-based method (EWM). This new method was applied directly with regard to the real-life RPB, in both its healthy and damaged conditions. Also, its corresponding numerical model based on the finite element method in its healthy condition and different damage scenarios were carried out. The results indicate that the new EWM retains the advantages of WEAM, and it allows for damage identification to be completed more efficiently, increasing the precision by approximately 0.11%, and significantly reducing the computing time required to obtain results by 5.67 times.