Damage Detection for Underground Arch Structure Considering Errors in Baseline Model

Fei Zheng; Jin Yu Xu; Yong Chen; De Hui Zhao

doi:10.4028/www.scientific.net/AMR.163-167.2576

Paper Titles

Monitoring for Large Cross-Section CFSTs of a Super High-Rise Building with Piezoceramic Actuators and Sensors
p.2553

Study on the Numerical Simulation of Massive Concrete Sidewall Hydration Heat under Negative Temperature Transient Boundary
p.2560

Experimental Study on Temperature Fields of Dumbbell-Shape Section of CFST Arch Rib and its Effects
p.2564

Study on the Effect of Prestress to the Dynamic Characteristics of Truss String Structure
p.2571

Damage Detection for Underground Arch Structure Considering Errors in Baseline Model
p.2576

Acoustic Emission Monitoring and Fracture Process of Reinforced Concrete Beams Strengthened in Flexure with CFRP
p.2581

Influence of the Tension Variety on Non-Linear Natural Frequency of an Inclined Cable
p.2585

Analysis and Testing of Dynamic Characteristics of Xinguang Bridge
p.2592

Detection of Concrete Spalling Using Changes in Modal Flexibility
p.2598

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 163-167Damage Detection for Underground Arch Structure...

Damage Detection for Underground Arch Structure Considering Errors in Baseline Model

Abstract:

Underground arch structure is an important structure form of civil air defense engineering, and its damage detection is an important link in safety evaluation of civil air defense engineering. In this study, a neural networks based damage detection method using the modal properties is presented. Based on the analysis of the dynamic properties of underground arch structure before and after damage, it was proved that the ratio of mode shape was insensitive to finite element model errors. The variation rate of mode shape was taken as the signature for damage detection, and it was proved that the variation of mode shape caused by multiple damage state was transformed into superposition of single damage state. It is not needed to constitute multiple location damage patterns when using the neural networks. A typical underground arch structure is analyzed to demonstrate the effectiveness of the proposed method. When 10%’s modeling errors exists, the damage location and extent can be recognized well.