Dome structures have been built as roofs for venues where many people convene. Failure of this type of structure may jeopardize the safety of hundreds or even thousands people. For this type of structure, snap-through buckling may occur in a local area and gradually expand to the entire structure, leading to a failure of the overall structure. Although numerous structural health monitoring techniques and damage detection approaches have been developed, no research on the detection of a snap-through buckling has been reported. The objective of this study is to find a signature that is sensitive to snap-through buckling in dome structures and can be used to detect snap-through buckling. Considering that a snap-through buckling results in a significant deformation in a local area, which can be reflected by the change in tilting angles of members in that local area, the change in tilting angles of members will be proposed to be a signature to detect snap-through buckling. To verify the proposed instability signature, a reticulated dome structure will be investigated. Both an eigenvalue buckling analysis and a nonlinear buckling analysis will be conducted. The significant changes in tilting angles of members in the buckled regions have demonstrated the efficacy of the proposed instability signature. This research will bridge the research gap between structural health monitoring and structural stability research.
Recent years have witnessed a number of collapses of civil space structures, which have severely jeopardized the safety of the general public. Therefore, it is imperative to propose an approach that can localize damage to exact members at an early stage for space structures. Then, the obtained damage location results can be used by the maintenance/repair crew for taking timely actions. For most space structures, the member configuration possesses a regular pattern. For this type of structure, before damage occurs, the regular pattern in the member configuration is maintained. After damage occurs, the regular pattern around the damaged member will be destroyed. In this study, the proposed approach to locate damage is based on the change in the status of regularity of member configuration. Herein the difference in an angle of the triangular shape in the related region of a space structure is used to indicate whether the regularity of member configuration is maintained. To validate the proposed approach, a reticulated shell space structure will be investigated.
Artificial Neural Networks (ANNs) have been applied in structural damage detection as a classifier, but generally a capable ANNs has to be trained with a certain amount of samples. When both damage locations and damage extents are to be identified, the amount of training samples is tremendous because of the combinations of damage locations and extents. By wavelet transform of the structure free motion equations, the Residual Wavelet Coefficient Vector (RWCV) is deduced. A damage feature parameter is defined as the ratio between RWCVs in two different frequency bands. This parameter has a unique property that it's sensitive only to damage locations, and is independent of damage extents. The damage feature parameters are then fed to the neural network for damage localization. After the damage sites are detected, the damage extent is further identified by another neural network with RWCVs as inputs. This two-phase approach for damage localization and extent identification can simply the neural network and reduce the training samples tremendously. Finally a numerical example is given for damage detection of a 10 DOFs system using the proposed approach.