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This research proposes a unique approach for detecting damage locations and identifying damage kinds. This method is beneficial for discovering and categorizing internal structural faults that vision-based approaches cannot locate. Construction-related vibrations in a steel frame structure can be used as a source for acoustic emission. Sensor devices detect the stress waves produced by structure collapse, and spectrum analysis using wavelet transform of such data is valuable in pinpointing the location of the damage. The col-lected characteristics from these signals are input into the most effective RF (Random Forest) classifier, which are used to categories damage types like cracks and bolt loosening. When compared to previous damage localization approaches, the findings show that the proposed strategy is more efficient and has a higher classification accuracy.

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Steel structures are commonly utilized in vast areas in industries, and also now a days they are used in residential settings as well. Structures made of steel is a better alternative as their constructions have high strength, light weight and quick compared to other construction materials. Steel structure degradation is frequently related to an engineering system's underperformance and leads to collapse. Therefore, it is essential to identify the problem and take remedial steps to make sure that structures function as intended throughout their design lives. Among the best non-destructive assessment methods for finding problems is acoustic emission (AE). The current study evaluates the available literature on this method in a few major areas and discusses historical advances in each category. The pros and cons of each approach are discussed, and future study directions are suggested. This review examines the fundamental Acoustic Emission techniques and contemporary research to identify damage in different types of steel structures using various localization approaches. This research aims to find the ideal placement for a real-time sensor to detect deterioration in a steel-framed construction. Finally, the artificial intelligence techniques used to identify deterioration in the steel frame construction are discussed.