City University of Hong Kong, China

Email: bckclam@cityu.edu.hk

Qualifications

Ph.D., University of New South Wales, Australia

M.Sc., University of Manchester Institute of Science and Technology (UMIST), Britain

B.Sc., National Taiwan University, China

Publications (Selected)

1. Oshodi, O., Edwards, D. J., Lam, K. C., Olanipekun, A. O., & Aigbavboa, C. O. (2020). Construction output modelling: a systematic review. Engineering, construction and architectural management, 27(10), 2959-2991.
2. Idowu, O. S., & Lam, K. C. (2020). Conceptual quantities estimation using bootstrapped support vector regression models. Journal of Construction Engineering and Management, 146(4), 04020018.
3. Idowu, O. S., & Lam, K. C. (2019). Web-based application for predesign cost planning of vertical building envelopes. Automation in Construction, 106, 102909.
4. Oshodi, O. S., & Lam, K. C. (2018). Using an adaptive neuro-fuzzy inference system for tender price index forecasting: A univariate approach. In Fuzzy hybrid computing in construction engineering and management (pp. 389-411). Emerald Publishing Limited.
5. Lam, K. C., & Idowu, O. S. (2017, December). Modelling Conceptual Quantities Using Support Vector Machines. In International Conference on Construction and Quantity Surveying 2017 (ICCQS 2017).
6. Banks, S. J., Chan, D. H., Lee, K. H., & Lam, K. C. (2017). Triaxial testing of kaolin-cement mixture: Laboratory program and preliminary results. In Soft Soil Engineering (pp. 545-550). Routledge.
7. Ejohwomu, O. A., Oshodi, O. S., & Lam, K. C. (2017). Nigeria’s construction industry: barriers to effective communication. Engineering, Construction and Architectural Management, 24(4), 652-667.
8. Lam, K. C., & Oshodi, O. S. (2016). Using univariate models for construction output forecasting: Comparing artificial intelligence and econometric techniques. Journal of Management in Engineering, 32(6), 04016021.
9. Lam, K. C., & Oshodi, O. S. (2016). Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model. Engineering, Construction and Architectural Management, 23(3), 302-322.
10. Lam, K. C., Oshodi, O. S., & Lee, E. W. M. (2016, February). Forecasting construction demand: A comparison of Box-Jenkins and Support Vector machine model. In Proceedings of the 9th Construction Industry Development Board (CIDB) Postgraduate Conference (pp. 14-23).
11. Lam, K. C., & Oshodi, O. S. (2015). The capital budgeting evaluation practices (2014) of contractors in the Hong Kong construction industry. Construction Management and Economics, 33(7), 587-600.
12. Yung, P., Lam, K. C., & Yu, C. (2015). Impacts of placement and work experience on construction education. International Journal of Construction Project Management, 7(1), 33.
13. Yu, C., & Lam, K. C. (2014). Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction. Journal of Advanced Transportation, 48(3), 250-271.
14. Yu, C., Lam, K. C., & Yung, P. (2014). Factors that influence the concession period length for tunnel projects under BOT contracts. Journal of Management in Engineering, 30(1), 108-121.

Profile Details

https://scholars.cityu.edu.hk/en/persons/ka-chi-lam(c525ddd0-35a3-4435-ba7c-e0d87f810d7d).html

https://orcid.org/0000-0002-4573-6631

https://www.scopus.com/authid/detail.uri?authorId=35324530300

https://scholar.google.com/citations?user=hIwQg0QAAAAJ&hl=en