Ren Li

TL;DR: Wang et al. as mentioned in this paper proposed a novel Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection.

TL;DR: This work proposes a novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection, where CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long- term temporal dependencies jointly.

TL;DR: This article presents a novel model, called the bridge structure and health monitoring ontology, to achieve fine-grained modeling of bridge structures, SHM systems, sensors, and sensory data from multiple perspectives and uses a bridge SHM big data platform to demonstrate the usefulness.

TL;DR: A novel end-to-end structural damage detection neural model is proposed by taking the advantages of the Convolutional Neural Network and Bidirectional Gated Recurrent Unit in parallel and can achieve a better detecting effect than other existing manners.

TL;DR: A novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text and results show that the proposed model outperforms other mainstream NER models on the bridge inspection corpus.