The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Jack Chin Pang Cheng sits on this spectrum.
This scientist: 383 publications — 87th percentile
87% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 804 publications or more.
The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Jack Chin Pang Cheng sits on this spectrum.
This scientist: 75 D-Index — 93rd percentile
93% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 107 D-Index or more.
Jack Chin Pang Cheng is affiliated with the Hong Kong University of Science and Technology in China and has a research background primarily situated within the fields of Engineering and Computer Science. Their scholarly work extensively engages with topics related to Building and Construction, Civil and Structural Engineering, and various computational approaches within these domains.
The researcher's notable publication record includes contributions to prominent venues such as Automation in Construction, Advanced Engineering Informatics, SSRN Electronic Journal, Kalpa publications in computing, and the Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC). Their work features a blend of theoretical and applied research, with particular emphasis on integration and optimization technologies for the construction industry.
Key research topics explored by Jack Chin Pang Cheng encompass:
Frequent collaborators include Xingyu Tao, H.L. Kwok, Peter Kok-Yiu Wong, Moumita Das, and Vincent J.L. Gan. Collaborative efforts with these co-authors have contributed to advancing interdisciplinary studies within construction engineering and intelligent infrastructure systems.
The following recent papers illustrate the scope and focus of their research output:
Their recent studies cover themes such as machine learning applications in predictive maintenance, blockchain frameworks for construction finance, energy-efficient building simulations, and digital twin technology for construction sites.
Md. Uzzal Hossain;Chi Sun Poon;Irene M.C. Lo;Jack C.P. Cheng
Jack C.P. Cheng;Weiwei Chen;Keyu Chen;Qian Wang
Jack Chin Pang Cheng;Lauren Y.H. Ma
Xin Liu;Xiangyu Wang;Xiangyu Wang;Graeme Wright;Jack Chin Pang Cheng
Jack Chin Pang Cheng;Mingzhu Wang
Minkoo Kim;Minkoo Kim;Jack Chin Pang Cheng;Hoon Sohn;Chih-chen Chang
Yichuan Deng;Jack Chin Pang Cheng;Chimay J. Anumba
Jack Chin Pang Cheng;Kincho Law;Hans J. Bjornsson;Albert T. Jones
Min-Koo Kim;Qian Wang;Qian Wang;Joon-Woo Park;Jack Chin Pang Cheng
Jun Ma;Jack C.P. Cheng;Changqing Lin;Yi Tan
Yongze Song;Xiangyu Wang;Yi Tan;Peng Wu
Md. Uzzal Hossain;Chi Sun Poon;Irene M.C. Lo;Jack C.P. Cheng
Srinath Shiv Kumar;Jack Chin Pang Cheng
Weiwei Chen;Keyu Chen;Jack C.P. Cheng;Qian Wang
Jongsung Won;Jack Chin Pang Cheng;Ghang Lee
Qiqi Lu;Jongsung Won;Jack Chin Pang Cheng
Jongsung Won;Jack Chin Pang Cheng
Jun Ma;Jack Chin Pang Cheng
Jack Chin Pang Cheng;Qiqi Lu
Jun Ma;Yuexiong Ding;Jack C.P. Cheng;Feifeng Jiang
Jack Chin Pang Cheng;Qiqi Lu;Yichuan Deng
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