New Damage Identification Method for Operational Metro Tunnel Based on Perturbation Theory and Fuzzy Logic

Abstract

The structural health of operational metro tunnels is closely related to public safety. Prior research has focused on the locations of structural damage, but few researchers have examined both the location of damage and identifying the degree of damage, especially in metro shield tunnels. This paper proposes a new method for identifying structural damage that entails locating and detecting the degradation of tunnel performance, with a special focus on characterizing the degree of damage. First, the dynamic behaviors (modal frequencies and shapes) of different damage levels are obtained from an analytical model of the original tunnel structure. Second, a Modal Strain Energy Damage Indicator (MSEDI) is introduced to locate the damage, regardless of size. Once the location of the damage is identified using MSEDI, a fuzzy logic-based damage identification (FLBDI) method is used to determine the actual extent of the damage.Finally, a simplified model of the tunnel is created using the Euler-Bernoulli beam theory and Winkler's foundation, to further test the procedure under an incomplete modal information condition and with differing noise levels. The results reveal that the fuzzy logic-based system can identify the degree of damage and structural degradation with very high accuracy, in which the location of damage and the prediction of performance degradation is satisfactorily confirmed.

Plugging Characteristics and Evaluation Predicting Models by Controllable Self-Aggregation Nanoparticles in Pore Throat Microcapillaries

“…It is currently the most effective analytical method for complex multi-factorial relationship problems. 18 Currently, the gene expression algorithm has been used in the prediction of dynamic modulus of asphalt mixtures, 19 the prediction of bond strength of concrete, 20 cost estimation of subway construction projects, 21 and depth prediction of abrasive jet cutting, 22 among other related research, and ideal predictive models have been established to improve prediction accuracy. This paper for the first time applies this algorithm to the field of oilfield development technology to analyze predictive models under multiple influencing factors.…”