http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Liangding Li (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Anti-collapse performance analysis of unequal span steel–concrete composite substructures
Bao Meng,Liangde Li,Wei-hui Zhong,Zheng Tan,Yu-hui Zheng 국제구조공학회 2021 Steel and Composite Structures, An International J Vol.39 No.4
In the study, three 1:3-scale unequal span steel–concrete composite substructures with top-seat angle and double web angle connection were designed and identified as specimens GTSDWA-0.6, GTSDWA-1.0, and GTSDWA-1.4. Pseudo-static tests and refined numerical model analysis were conducted to examine the anti-progressive collapse performance of a semi-rigid steel–concrete composite substructure. The results indicated that the failure modes of the three specimens revealed that the fracture occurred in the root of the long leg of the top/seat angle in tension at the connection. With increases in the span ratio of the left and right composite beams, the bearing capacities of the composite substructures decreased, and the corresponding displacement increased. With respect to GTSDWA-0.6 and GTSDWA-1.4, the resistance due to the short composite beam corresponded to 62% and 60%, respectively, and the total resistance provided by the short composite beam exceeded that of the long composite beam. With respect to GTSDWA-1.0, the resistance due to the left and right composite beams was similar. All three specimens underwent the flexure mechanism and flexure–axial mixed mechanism stages. They resisted the external load mainly via the flexure mechanism. Moreover, the addition of stiffeners on both sides of the top and seat angles is advantageous in terms of improving the collapse resistance and ductility of unequal span composite substructures.
-
Zhiming Zhang,Jin Yan,Liangding Li,Hong Pan,Chuan-Zhi Dong 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
