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397 results on '"Liu, Shaoqing"'

51. Pozzolanic Analysis of GFRP Powder from Retired Wind Turbine Blades as Supplementary Cementitious Material.

Author

Liu, Shaoqing, Tam, Lik-ho, Ma, Hongqiang, Guo, Jianping, and Wu, Chao

Subjects
*WIND turbine blades, *GLASS fibers, *POWDERED glass, *HEAT of hydration, *SCANNING electron microscopes, *MORTAR
Abstract

The increasing demand in renewable wind energy has led to more retired wind turbine blades which are mainly made by glass fiber reinforced polymer (GFRP) composite. However, the recycling methods of these waste blades are limited so the blades have to be landfilled. This paper proposes a new recycling method which is developing the GFRP powder as a supplementary cementitious material (SCM), because the glass fibers in the powder are rich in silica and may have pozzolanic reactivity. This paper presents a comprehensive pozzolanic analysis of the GFRP powder and compares with reference SCMs of blast furnace slag (BFS) and fly ash (FA). The calcium oxide content in GFRP (12.56 wt.%) is significantly lower than in BFS (39.96 wt.%). Similarly, the aluminum oxide content in GFRP (10.54 wt.%) is lower compared to BFS (15.38 wt.%) and FA (33.17 wt.%). The particle size of GFRP is also larger than BFS and FA, and the glass fibers are covered by organic resin. The pozzolanic reactivity was analyzed by hydration heat, thermogravimetric (TG), scanning electron microscope (SEM), compressive strength test and mercury intrusion porosimeter (MIP). It was found that the resin in the GFRP powder reacts in alkaline environment and generates gas which increases the mortar porosity and reduces compressive strength. GFRP powder has longer induction period and generates fewer hydration products. The lower pozzolanic reactivity of the GFRP powder is mainly due to the presence of the resin and its coating on the glass fibers. Therefore, it is essential to remove the resin to increase the pozzolanic reactivity of the GFRP powder which has the potential to be developed as a new SCM. [ABSTRACT FROM AUTHOR]

Published
2025
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54. Analytical assessment of low‐ and high‐cycle fatigue behavior of welded components considering the linear hardening model.

Author

Liu, Shaoqing, Lei, Linshen, Gu, Tang, Lan, Tian, Wang, Chengqi, Su, Yangxuan, Wang, Lingjun, and Liu, Xiaochao

Subjects
*STRAINS & stresses (Mechanics), *FATIGUE life, *STRESS concentration, *WELDED joints, *ANALYTICAL solutions
Abstract

Welded components are highly vulnerable to fatigue failures due to stress concentrations and material imperfections. This study develops an analytical framework that predicts both high‐cycle (HCF) and low‐cycle (LCF) fatigue lives of welded joints using a structural strain parameter. By employing a linear hardening model, the method effectively translates structural stress into structural strain, providing a reliable metric for assessing fatigue life, especially in LCF conditions. Validated against experimental data from 62 longitudinal gusset joints, the approach proves both reliable and practical. This simplified method facilitates easier fatigue assessments, encouraging its wider use and potential standardization in engineering, thus improving the predictability of welded structures' performance. Highlights: An analytical framework is presented to assess the fatigue behavior of welded structures.The linear hardening law is introduced to capture the material hardening behavior.The analytical solutions for plane stress and plane strain conditions are provided.The method is applied to analyze 62 weldment fatigue data in both HCF and LCF regime. [ABSTRACT FROM AUTHOR]

Published
2024
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57. Repolarization of immunosuppressive macrophages by targeting SLAMF7-regulated CCL2 signaling sensitizes hepatocellular carcinoma to immunotherapy

Author

Weng, Jialei, primary, Wang, Zheng, additional, Hu, Zhiqiu, additional, Xu, Wenxin, additional, Sun, Jia-Lei, additional, Wang, Fu, additional, Zhou, Qiang, additional, Liu, Shaoqing, additional, Xu, Min, additional, Xu, Minghao, additional, Gao, Dongmei, additional, Shen, Ying-Hao, additional, Yi, Yong, additional, Shi, Yi, additional, Dong, Qiongzhu, additional, Zhou, Chenhao, additional, and Ren, Ning, additional

Published
2024
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59. Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

Author

Gong, Binsheng, Li, Dan, Kusko, Rebecca, Novoradovskaya, Natalia, Zhang, Yifan, Wang, Shangzi, Pabón-Peña, Carlos, Zhang, Zhihong, Lai, Kevin, Cai, Wanshi, LoCoco, Jennifer S., Lader, Eric, Richmond, Todd A., Mittal, Vinay K., Liu, Liang-Chun, Johann, Jr, Donald J., Willey, James C., Bushel, Pierre R., Yu, Ying, Xu, Chang, Chen, Guangchun, Burgess, Daniel, Cawley, Simon, Giorda, Kristina, Haseley, Nathan, Qiu, Fujun, Wilkins, Katherine, Arib, Hanane, Attwooll, Claire, Babson, Kevin, Bao, Longlong, Bao, Wenjun, Lucas, Anne Bergstrom, Best, Hunter, Bhandari, Ambica, Bisgin, Halil, Blackburn, James, Blomquist, Thomas M., Boardman, Lisa, Burgher, Blake, Butler, Daniel J., Chang, Chia-Jung, Chaubey, Alka, Chen, Tao, Chierici, Marco, Chin, Christopher R., Close, Devin, Conroy, Jeffrey, Cooley Coleman, Jessica, Craig, Daniel J., Crawford, Erin, del Pozo, Angela, Deveson, Ira W., Duncan, Daniel, Eterovic, Agda Karina, Fan, Xiaohui, Foox, Jonathan, Furlanello, Cesare, Ghosal, Abhisek, Glenn, Sean, Guan, Meijian, Haag, Christine, Hang, Xinyi, Happe, Scott, Hennigan, Brittany, Hipp, Jennifer, Hong, Huixiao, Horvath, Kyle, Hu, Jianhong, Hung, Li-Yuan, Jarosz, Mirna, Kerkhof, Jennifer, Kipp, Benjamin, Kreil, David Philip, Łabaj, Paweł, Lapunzina, Pablo, Li, Peng, Li, Quan-Zhen, Li, Weihua, Li, Zhiguang, Liang, Yu, Liu, Shaoqing, Liu, Zhichao, Ma, Charles, Marella, Narasimha, Martín-Arenas, Rubén, Megherbi, Dalila B., Meng, Qingchang, Mieczkowski, Piotr A., Morrison, Tom, Muzny, Donna, Ning, Baitang, Parsons, Barbara L., Paweletz, Cloud P., Pirooznia, Mehdi, Qu, Wubin, Raymond, Amelia, Rindler, Paul, Ringler, Rebecca, Sadikovic, Bekim, Scherer, Andreas, Schulze, Egbert, Sebra, Robert, Shaknovich, Rita, Shi, Qiang, Shi, Tieliu, Silla-Castro, Juan Carlos, Smith, Melissa, López, Mario Solís, Song, Ping, Stetson, Daniel, Strahl, Maya, Stuart, Alan, Supplee, Julianna, Szankasi, Philippe, Tan, Haowen, Tang, Lin-ya, Tao, Yonghui, Thakkar, Shraddha, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkat J., Thomas, David, Tichý, Boris, Tom, Nikola, Garcia, Elena Vallespin, Verma, Suman, Walker, Kimbley, Wang, Charles, Wang, Junwen, Wang, Yexun, Wen, Zhining, Wirta, Valtteri, Wu, Leihong, Xiao, Chunlin, Xiao, Wenzhong, Xu, Shibei, Yang, Mary, Ying, Jianming, Yip, Shun H., Zhang, Guangliang, Zhang, Sa, Zhao, Meiru, Zheng, Yuanting, Zhou, Xiaoyan, Mason, Christopher E., Mercer, Timothy, Tong, Weida, Shi, Leming, Jones, Wendell, and Xu, Joshua

Published
2021
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65. Calculation Analysis on Traction Motor Temperature Rise of EMU Vehicles Based on Fuzzy Neural Network

Author

Liang, Jianying, Liu, Shaoqing, Zhong, Chongcheng, Yu, Jin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Samad, Tariq, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Limin, editor, Qin, Yong, editor, Suo, Jianguo, editor, Feng, Jianghua, editor, Diao, Lijun, editor, and An, Min, editor

Published
2018
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68. Tuning and optimization of two-phase absorbents (DEEA/AEEA/H2O) with hybrid phase splitter (n-butanol/DEEA) for several properties: Carbon capture, phase separation, physical properties

Author

Jin, Lijian, primary, Hou, Xueyan, additional, Zhan, Lingxiao, additional, Hou, Dawei, additional, Gu, Lina, additional, Zhang, Daguang, additional, Shen, Jianchong, additional, Zheng, Zhihao, additional, Lv, Chao, additional, Liu, Shaoqing, additional, and Yang, Linjun, additional

Published
2023
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75. Relationship between Thermal Conductivity and Compressive Strength of Insulation Concrete: A Review

Author

Wu, Chao, Liu, Shaoqing, Guo, Jianping, Ma, Hongqiang, He, Li, Wu, Chao, Liu, Shaoqing, Guo, Jianping, Ma, Hongqiang, and He, Li

Abstract

Developing insulation concrete with high strength is essential for the construction of energy saving buildings. This is important to achieve carbon neutrality in the modern building industry. This paper reviews the existing studies in the literature on insulation concrete. This paper aims to reveal the correlation between the thermal conductivity and strength of concrete and identify the most effective method to make insulation concrete with lower thermal conductivity but higher strength. The review is carried out from two perspectives, including the effects of different foaming methods and various lightweight aggregates. As for the foaming methods, the chemical and mechanical foaming methods are discussed. As for the lightweight aggregates, cenospheres, porous aggregates, aerogels, and phase change materials are assessed. It is clearly observed that the thermal conductivity and compressive strength of concrete can be fitted by a linear function. As for the foaming methods, chemical foaming using hydrogen peroxide is the most effective to produce concrete with relatively lower thermal conductivity and higher compressive strength. For concrete with lightweight aggregates, cenospheres are the best option. Finally, recommendations are made to develop concrete with lower thermal conductivity and higher strength.

Published
2023

77. Study on optimization of pouring time for late poured band

Author

Xie, Liang, Tang, Bin, Liu, Shaoqing, Gong, Yanxia, and Cheng, Xunchao

Subjects
Urban Studies, Geography, Planning and Development, Building and Construction
Abstract

The setting of the late poured band is an effective measure to control the differential settlement and the stress concentration of the foundation. However, the lag of the pouring time of the late poured band will also bring adverse effects to the construction process. Taking a large commercial complex as an example, Midas GTS NX finite element software is used for the overall foundation modeling, and the collaborative calculation of settlement is carried out by adjusting the composite foundation scheme. The calculation results of conventional codes are compared and analyzed, and the scheme is verified and optimized. Then, the variation value of differential settlement on both sides of the late poured band with loading is studied by simulating the graded loading of each area in the whole process of construction. By comparing the variation value of differential settlement and the calculation value of differential deformation that the stiffness of the late poured band can resist and the limit value of codes, the time node of late poured band pouring in advance can be obtained, and finally the influence of early pouring of the late poured band on the collaborative settlement of the foundation is verified and calculated. The results show that: the settlement calculation of finite element analysis is generally smaller than that of standard calculation; The settlement at the edge of the raft is small, and the settlement at the center of the raft and the places with large load is large; Considering the interaction of the whole raft and subgrade under the raft, this method can verify and optimize the design scheme of composite foundation and save the pouring time of the late poured band. The experience and achievements so made can be used as a reference for similar projects.

Published
2023
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81. Tuning and Optimization of Tow-Phase Absorbents(Deea/Aeea/H2o) with Hybrid Phase Splitter (N-Butanol/Deea) for Several Properties: Carbon Capture, Phase Separation, Physical Properties

Author

Jin, Lijian, primary, Hou, Xueyan, additional, Zhan, Lingxiao, additional, Hou, Dawei, additional, Gu, Lina, additional, Zhang, Daguang, additional, shen, Jianchong, additional, Zheng, Zhihao, additional, Lv, Chao, additional, Liu, Shaoqing, additional, and Yang, Linjun, additional

Published
2023
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89. An Improved Deep Transfer Learning Method for Rotating Machinery Fault Diagnosis Based on Time Frequency Diagram and Pretraining Model

Author

Liu, Shaoqing, Ji, Zhenshan, Zhang, Zuchao, and Wang, Yong

Abstract

Under varying working conditions, the failure prediction model for the same type of equipment often proves ineffective in its deployment and application. Aiming to address the limitations of current fault diagnosis models based on neural networks, such as limited depth, insufficient feature extraction ability, lack of adaptive ability, and poor classification effectiveness in various domains, a novel fault prediction algorithm called deep adaption residual neural network (DARN) was studied and proposed. The algorithm incorporates a pretraining model to enhance its fault diagnosis capabilities. In this method, the time frequency diagram of the original time-series signal is obtained through time frequency processing. At the same time, the residual neural network pretraining model is used as the primary network for feature extraction. In addition, several loss functions are designed to minimize the discrepancy between data categories and the loss of adaptive transfer. The ablation experiments for several hyperparameters were carried out. The proposed method not only improves the accuracy of the fault prediction model but also significantly reduces the training time. Compared with the traditional neural network fault diagnosis model, this method addresses the issues of structural instability and limited feature extraction ability. It ensures that the model maintains strong predictive capabilities across various working conditions. Finally, the method was validated on a public bearing dataset and a homemade bearing dataset.

Published
2024
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