Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Gaoyang, Liu"'
Publikováno v:
Case Studies in Construction Materials, Vol 21, Iss , Pp e03648- (2024)
Concrete in frigid northern regions is prone to the detrimental impacts of freeze-thaw cycles, leading to the development of cracks and spalling, which affects structural safety and durability. This research introduces a novel method for predicting c
Externí odkaz:
https://doaj.org/article/b3b9398b3bc743ddbdc6f4f61d6c1c0d
Autor:
Mingxuan Du, Chengjia Zhao, Haiyan Hu, Ningning Ding, Jiankang He, Wenwen Tian, Wenqian Zhao, Xiujian Lin, Gaoyang Liu, Wendan Chen, ShuangLiu Wang, Pengcheng Wang, Dongwu Xu, Xinhua Shen, Guohua Zhang
Publikováno v:
BMC Psychology, Vol 12, Iss 1, Pp 1-22 (2024)
Abstract A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this
Externí odkaz:
https://doaj.org/article/0c3e9d45ea6743afa9782f4b9bb48b13
Publikováno v:
Journal of CO2 Utilization, Vol 85, Iss , Pp 102877- (2024)
With the development of the circular economy and low-carbon society, the large-scale application of construction solid waste in buildings, such as recycled concrete, is becoming imperative. Accurately predicting the carbonation depth of recycled conc
Externí odkaz:
https://doaj.org/article/dc2f8072c8f04f778993b606c97bc7b9
Autor:
Jiangpeng Shu, Hongchuan Yu, Gaoyang Liu, Han Yang, Wei Guo, Chinyong Phoon, Strauss Alfred, Hao Hu
Publikováno v:
Case Studies in Construction Materials, Vol 20, Iss , Pp e03350- (2024)
Advanced machine learning (ML) models are utilized for accurate shear strength prediction of reinforced concrete beams (RCB), but their lack of interpretability makes it unclear how models make specific predictions, reducing their reliability and app
Externí odkaz:
https://doaj.org/article/1909bf81ce81486fa6afae6a76b880f3
Publikováno v:
Space: Science & Technology, Vol 4 (2024)
In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and st
Externí odkaz:
https://doaj.org/article/794c5755c8e94d7d9de30449d352b751
Publikováno v:
Journal of Infrastructure Preservation and Resilience, Vol 4, Iss 1, Pp 1-14 (2023)
Abstract Compared with acceleration-based modal analysis, displacement can provide a more reliable and robust identification result for output-only modal analysis of long-span bridges. However, the estimated displacements from acceleration records ar
Externí odkaz:
https://doaj.org/article/4b7fc69de25046309862d0a4ace949a5
Publikováno v:
Buildings, Vol 14, Iss 4, p 983 (2024)
Component identification and depth estimation are important for detecting the integrity of post-disaster structures. However, traditional manual methods might be time-consuming, labor-intensive, and influenced by subjective judgments of inspectors. D
Externí odkaz:
https://doaj.org/article/dbe7b8dd47d5431092f61bf3066d4b11
Publikováno v:
Case Studies in Construction Materials, Vol 19, Iss , Pp e02405- (2023)
The precise prediction of concrete compressive strength is essential for ensuring safe and reliable infrastructure design and construction. However, traditional empirical models often struggle to accurately predict compressive strength due to the com
Externí odkaz:
https://doaj.org/article/9d33cc0329234726952c7e8602597349
Autor:
Gaoyang Liu, Bochao Sun
Publikováno v:
Case Studies in Construction Materials, Vol 18, Iss , Pp e01845- (2023)
The mixing ratio of the raw materials has a significant impact on concrete compressive strength. Although the compressive strength of concrete can be inferred from the mix ratio, it is frequently challenging to determine how each mix ratio parameter
Externí odkaz:
https://doaj.org/article/388fba3b99014bf699ecc43a07b345c9
Publikováno v:
Digital Communications and Networks, Vol 8, Iss 4, Pp 446-454 (2022)
Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine Learning (ML), where the ML model is trained in a decentralized manner by the clients, preventing the server from directly accessing privacy-sensitive data from the cli
Externí odkaz:
https://doaj.org/article/567c987e3b064a259a7e6fc5cfaa378b