周鹏

发布时间:2019-09-11来源: 浏览次数:

 

PENG ZHOU(周鹏)

Associate Professor

PhD,Construction Management, University of Illinois at Urbana-Champaign (UIUC)

Master,Construction Management, UIUC

Contact

Central University of Finance and Economics

Shahe Higher Education Park, Beijing, China 102206

Office:Building 4, Room 336

Email:zhoupeng@cufe.edu.cn;pzhoucufe@163.com

Research interests

Big Data Analytics and Computing in Low Carbon Emission and Energy Saving Infrastructures, Smart City, Artificial Intelligence

Appointments

Associate Professor,Department of Management Science and Engineering, Central University of Finance and Economics

Teaching

Introduction to Building Information Modeling (undergraduate)

Automated Cost Estimation (undergraduate)

Engineering Risk Management (undergraduate)

Infrastructure Investment and Development (graduate)

Professional English (graduate)

Selected publications

 Zhou, P., and El-Gohary, N. (2022). “Semantic information alignment of BIMs to computer-interpretable regulations using ontologies and deep learning.”Adv. Eng. Inform.,10.1016/j.aei.2020.101239.(SCI)

Zhang, Q., Xue, C.,Zhou, P., Wang, X., Zhang, J., and Su, X. (2022). “Named entity recognition for Chinese construction documents based on conditional random field.”Frontiers Eng. Manage.(SCI)

Zhou, P., and Chang, Y. (2021). “Automated classification of building structures for urban built environment identification using machine learning.”J. Build. Eng.,10.1016/j.jobe.2021.103008, 04015057.(SCI)

Zhou, P., and El-Gohary, N. (2017). “Ontology-based automated information extraction from building energy conservation codes.”Autom. Constr., 10.1016/j.autcon.2016.09.004. (SCI)

Zhou, P., and El-Gohary, N. (2015). “Domain-specific hierarchical text classification for supporting automated environmental compliance checking.”J. Comput. Civ. Eng.,10.1061/(ASCE)CP.1943-5487.0000513, 04015057.(SCI)

Zhou, P., and El-Gohary, N. (2015). “Ontology-based multi-label text classification for construction regulatory documents.”J. Comput. Civ. Eng.,10.1061/(ASCE)CP.1943-5487.0000530, 04015058.(SCI)

Zhou, P., and El-Gohary, N. (2018). “Text and information analytics for fully automated energy code checking.”Proc. 2nd GeoMEast Int. Congress and Exhibition on Sustainable Civil Infrastructures,Springer International Publishing, Cham, Switzerland, 196-208.

Zhou, P., and El-Gohary, N. (2018). “Automated matching of design information in BIM to regulatory information in energy codes.”Proc. 2018 ASCE Construction Research Congress (CRC), ASCE, Reston, VA, 75-85.

Zhou, P., and El-Gohary, N. (2016). “Automated extraction of environmental requirements from contract specifications.”Proc.16th Int. Conf. on Comput. in Civ. and Build. Eng. (ICCCBE2016),International Society for Computing in Civil and Building Engineering (ISCCBE), Osaka, Japan, 1669-1676.

Zhou, P., and El-Gohary, N. (2015). “Ontology-based information extraction from environmental regulations for supporting environmental compliance checking.”Proc.2015 ASCE Int. Workshop Comput. Civ. Eng., ASCE, Reston, VA, 190-198.

Zhou, P., and El-Gohary, N. (2014). “Ontology-based multi-label text classification for enhanced information retrieval for supporting automated environmental compliance checking.”Proc. Comput. in Civ. and Build. Eng. (2014), ASCE, Reston, VA, 2238-2245.

Zhou, P., and El-Gohary, N. (2014). “Semantic-based text classification of environmental regulatory documents for supporting automated environmental compliance checking in construction.”Proc. 2014 ASCE Construction Research Congress (CRC), ASCE, Reston, VA, 897-906.

Research project

National Natural Science Foundation of China

Professional services

ReviewerJournal of Computing in Civil Engineering, Journal of Environmental Management, China Civil Engineering Journal

CommitteeData Sensing and Analysis (DSA) committee, Visualization, Information Modeling and Simulation (VIMS) committee, American Society of Civil Engineers (ASCE), TRB Information Systems in Construction Management Subcommittee.

Recruitment

Graduate students with major, talents or interest in application of machine learning, deep learning, computer vision, and natural language processing techniques to engineering management domain are welcome to join my research team.

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