A new neural-network-based method for structural damage identification in single-layer reticulated shells

Single-layer reticulated shells(SLRSs)find widespread application in the roofs of crucial public structures,such as gymnasiums and exhibition center.In this paper,a new neural-network-based method for structural damage identification in SLRSs is proposed.First,a damage vector index,NDL,that is related only to the damage localization,is proposed for SLRSs,and a damage data set is constructed from NDL data.On the basis of visualization of the NDL damage data set,the structural damaged region locations are identified using convolutional neural networks(CNNs).By cross-dividing the damaged region locations and using parallel CNNs for each regional location,the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated.Second,a damage vector index,DS,that is related to the damage location and damage degree,is proposed for SLRSs.Based on the damaged region identified previously,a fully connected neural network(FCNN)is constructed to identify the location and damage degree of members.The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS.The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

This paper proposes a framework for critical element identification and demolition planning of frame structures.Innovative quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learning and graph embedding.The action is defined as removing an element,and the state is described by integrating the joint and element features into a comprehensive feature vector for each element.By establishing the policy network,the agent outputs the Q value for each action after observing the state.Through numerical examples,it is confirmed that the trained agent can provide an accurate estimation of the Q values,and handle problems with different action spaces owing to utilization of graph embedding.Besides,different behaviors can be learned by varying hyperparameters in the reward function.By comparing the proposed method and the conventional sensitivity index-based methods,it is demonstrated that the computational cost is considerably reduced because the reinforcement learning model is trained offline.Besides,it is proved that the Q values produced by the reinforcement learning agent can make up for the deficiencies of existing indices,and can be directly used as the quantitative index for the decision-making for determining the most expected collapse scenario,i.e.,the sequence of element removals.