Nonparametric damage detection and localization model of framed civil structure based on local gravitation clustering analysis

Structural damage detection is still a stimulating problem due to the complicated non-linear behaviour of the structural system, incomplete sensed data, presence of noise in the data, and uncertainties in both experimental measurement and analytical model. This paper presents the application of a non-linear signal processing tool and artificial intelligence-based methodology for nonparametric damage detection to address the above stated issues. Local mean decomposition (LMD), as an adaptive signal processing technique, is exploited to extract multidimensional damage features over acquired non-linear non-stationary vibration signals. These features are classified into categories, which are then utilized to calculate a damage indicator. Classification of multidimensional feature space has been a challenging issue since its inception. To address this, local gravitation clustering (LGC), a self-evaluating, synergic clustering technique, is employed. The relevance and significance of the process corresponding to the problem have also been an important concern. The outcomes of the whole process prove its proficiency in damage identification. The efficiency of the process is then compared with existing clustering methods on several parameters. The proposed algorithm is also validated for operational and environmental conditions by considering finite cases analogues to physical ailments like temperature, ageing and live loads.

Automated summary

This paper introduces a method for nonparametric damage detection using a non-linear signal processing tool and artificial intelligence-based methodology, utilizing local mean decomposition and local gravitation clustering to extract and classify multidimensional damage features. The effectiveness of the method in damage identification is demonstrated through experiments, and its efficiency is compared with existing clustering methods.