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Ming Lu, PhD, PEng

Professor

Engineering

Civil and Environmental Engineering

About Me

Ming Lu, PhD, PEng, is Professor of Construction Engineering and Management (CEM) in the Department of Civil and Environmental Engineering, University of Alberta. As author/coauthor of over 75 papers in CEM-related top journals plus over 130 papers in peer-reviewed conference proceedings, Dr. Lu is widely known in the academic community of construction for contributions in (1) quantitative methods for construction engineering and management (2) computing in civil engineering, (3) automation in construction, and (4) education in construction. Among all the PhD students Dr. Lu supervised (co-supervised) based in Hong Kong and Alberta, five have become faculties employed at well recognized universities in US, Australia and Hong Kong. 

Since Sept. 2010, Dr. Lu has led the continuous growth of the Construction Automation Lab (AutoLab) at U of A, turning it into a unique academia-industry collaboratory for conducting applied research in integration, automation and optimization in construction engineering and project management.

Dr. Lu is an active member with American Society of Civil Engineers, Construction Institute (ASCE/CI); Association for Advancement in Cost Engineering International (AACEi); Alberta Professional Engineers and Geoscientists Association (APEGA).

Academic Degrees

  • Oct. 2000 Ph.D. in Civil Engineering – Construction Engineering & Management, the University of Alberta, Edmonton, Canada
  • Jun, 1994 B.Eng. (Honors) – Road & Traffic Engineering, Tongji University, Shanghai, China

Professional Registration 

  • Feb. 2013 Professional Engineer (PEng), Alberta Professional Engineers and Geoscientists of Alberta (APEGA)

Academic Experiences

  • Jan. 2017 – Full Professor in the Department of Civil and Environmental Engineering, the University of Alberta, Canada 
  • Sept. 2010 – Dec. 2016 Associate Professor (Tenured) in the Department of Civil and Environmental Engineering, the University of Alberta, Canada;
  • Apr. 2008 – Aug. 2010 Associate Professor of the Department of Civil and Structural Engineering, Hong Kong Polytechnic University; 
  • Nov. 2000 – Mar. 2008 Assistant Professor of the Department of Civil and Structural Engineering, Hong Kong Polytechnic University; 
Industry Experiences

  • Oct. 1996 - Aug. 1997 Assistant Project Manager, Project Development Department, Shell Oil (China) Limited. Beijing, China; as client’s representative, participated in planning and preliminary contract and construction management of Shell’s retail network in North China.
  • Aug. 1994 - Oct. 1996 Project Engineer, Department of Overseas Project Development, China Road & Bridge Corporation, Beijing, China; two years of site experience on road construction and rehabilitation projects in the Republic of Yemen.


Research

Winner of Best Paper Awards

  1. Hasan, Monjurul; *Lu, Ming (2017). "Error Quantification and Visualization in Using Sensors to Position Backhoe Excavator" ASCE International Workshop on Computing in Civil Engineering in 2017, Seattle, WA, Jun 25-27, 2017. selected for publication in an ASCE book "Computing in Civil Engineering in 2017: Smart Safety, Sustainability, and Resilience.", Lin, KY, El-Gohery, N, and Tang, P. (Eds) pp 150-157.
  2. Mao, S., Shen, X., Lu, M. & Wu, X. (2013). Real-time tablet-based virtual reality implementation to facilitate tunnel boring machine steering control in tunnel construction. In: N. Dawood and M. Kassem (Eds.), Proceedings of the 13th International Conference on Construction Applications of Virtual Reality, pp. 219-229, 30-31 October 2013, London, UK. 
  3. Siu, M. F., Mao, S., Lu, M. and AbouRizk, S. (2012) "Photogrammetric modeling and sling length measurements of a rigging system engineered for industrial module erection." Proceedings of 12th International Conference on Construction Applications of Virtual Reality (CONVR 2012), 340-349 (Winner of International Conference Travel Grant Award).
  4. Shen, X.S., Lu, M., Chen W., and Chan W. (2010). “Automatically tracking and guiding underground tunnel boring machines during microtunneling and pipe jacking operations.” Proceedings of the 2010 Construction Research Congress (2010 CRC), ASCE, May 8-11, 2010, Banff, Alberta, Canada.

Social Impact and media releases

In 2017, Dr. Lu led the research in developing a pragmatic mixed reality platform for construction engineering and management. In collaboration with industry partners in Alberta, Canada, the innovation was implemented on four case studies based on real world projects in throughout 2016 and 2017. The case studies were jointly funded through a cluster of graduate internships from MITACS. Talent training and research deliverables were featured by MITACS.CA and the national media in June 2017 as a success story of innovation resulting from cooperation between university and the construction industry in celebration of the 150th anniversary of Canada. Alberta Construction Magazine: Ledcor, UofA researchers blend virtual with real world imagery for remote project management. The academic contribution is published in Automation in Construction:

  • Duanshun Li, Ming Lu. Integrating geometric models, site images and GIS based on Google Earth and Keyhole Markup Language [J]. Automation in Construction, 89(2018):317-331. https://doi.org/10.1016/j.autcon.2018.02.002

In 2013, Dr. Lu’s research team implemented complete construction automation solutions to facility tunnel construction in a drainage tunnel site. This achievement has been hailed as “researchers tunnel path from lab to real world” (ualberta portal; ENR; news and TV media).

In 2013, Dr. Lu is the recipient of Fiatech 2014 Superior Technical Achievement Recognition (STAR) Award. Fiatech is the world-leading industry-academic consortium representing the largest owner, contractor and technology companies and the major research universities in the US and Canada to promote development and implementation of fully integrated and automated technology for construction industry. 

In 2009, Dr Lu cooperated with utility industry in Hong Kong to materialize the automation of micro tunnel boring machine tracking and tunnel alignment control.   

  • Trenchless International – the official magazine of the International Society of Trenchless Technology and The South China Morning Post – the leading English newspaper in Hong Kong reports Dr. Lu’s research in microtunneling in Mar. 2009. 

In Nov. 2009, the Dean of Construction and Land Use at Hong Kong Polytechnic University honored Dr Lu with the Award for Outstanding Publication Achievement in recognition of the significant collective impact made by Dr Lu’s outstanding journal papers in the discipline of Construction Engineering & Management.

  • Newsletter of Faculty of Construction and Land Use (FCLU), Hong Kong Polytechnic University, Issue No. 9, Dec. 2009, page 9.

During 2005-2006, Dr Lu advised Beijing Urban Construction Group on how to optimize the steel installation sequence and space use in the construction of the “Bird’s Nest” (Beijing Olympic Stadium) through computer simulation.

  • Newsletter of Faculty of Construction and Land Use (FCLU), Hong Kong Polytechnic University, Issue No. 9, Dec. 2009, page 5.

Theoretical foundation publications

  1. Lu, M., AbouRizk, S., and Hermann, U. (2001), “Sensitivity analysis of neural networks in spool fabrication productivity studies”, Journal of Computing in Civil Engineering, ASCE, Vol 15(4), 299-308.
  2. Lu, M., AbouRizk, S., and Hermann, U.(2000), “Estimating labor productivity using probabilistic inference neural network”, Journal of Computing in Civil Engineering, ASCE, 14/4, 241-248.
  3. Lu, M., and AbouRizk, S. (2000), “Simplified CPM/PERT simulation model”, Journal of Construction Engineering and Management, ASCE, 126/3, 219-226.
  4. Lu, M., and AbouRizk, S. (2001) “Closure to “Simplified CPM∕PERT Simulation Model” by Ming Lu and Simaan AbouRizk” Journal of Construction Engineering and Management 12/2001; 127(6). DOI:10.1061/(ASCE)0733-9364(2001)127:6(515) 
  5. Lu, M. (2002), “Enhancing PERT simulation through ANN-based input modeling”, Journal of Construction Engineering and Management, ASCE, 128 (5), 438-445.
  6. Lu,M. (2003), “Simplified discrete-event simulation approach for construction simulation”, Journal of Construction Engineering and Management, ASCE, 129(5), 537-546
  7. Lu, M. and Li, H. (2003), “Resource-activity critical path method for planning construction operations”, Journal of Construction Engineering and Management, ASCE, 129(4), 412-420.
  8. Lu, M., W. H. Chan, Zhang, J.P. and Cao M. (2007) “Generic process mapping and simulation methodology for integrating site layout and operations planning in construction”. Journal of Computing in Civil Engineering, ASCE, 21(6), 453-462.
  9. Lu, M and Lam, H.C. (2008) “Critical path scheduling under resource calendar constraints”, Journal of Construction Engineering and Management, ASCE, 134(1), 25-31.
  10. Lu, M., Liu, J. and Ji, W.Y. (2016) “Formalizing a Path-Float Based Approach to Determine and Interpret Total Float in Project Scheduling Analysis”, International Journal of Construction Management, Taylor & Francis · July 2016 DOI:10.1080/15623599.2016.1207366.  

Selected journal publications (based on PhD supervision)

  1. Liu, Jing, Lu, M. (2018) “"Constraint Programming Approach to Optimizing Project Schedules under Material Logistics and Crew Availability Constraints ", Journal of Construction Engineering and Management, ASCE DOI: 10.1061/(ASCE)CO.1943-7862.0001507
  2. Mohsenijam, Arash, Siu, M., and Lu, M. (2016). "Modified Stepwise Regression Approach to Streamlining Predictive Analytics for Construction Engineering Applications." J. Comput. Civ. Eng., 10.1061/(ASCE)CP.1943-5487.0000636, 04016066.
  3. Yi, Chaojue, Lu, M. (2016) “A mixed integer linear programming approach to design temporary haul roads in construction projects”, Automation in Construction, Elsevier Vol 71 (2016) 314-324.
  4. Li, Duanshun, Lu, M. (2016) “"Automated Generation of Work Breakdown Structure and Project Network Model for Earthworks Project Planning - A Flow Network Based Optimization Approach", Journal of Construction Engineering and Management, ASCE 04016086-1 to 04016086-1 -17
  5. Liu, Hexu, G. Singha , Ming Lu, Ahmed Bouferguene, Mohamed Al-Hussein. BIM-based automated design and planning for boarding of light-frame residential buildings [J]. Automation in Construction, 89(2018):235-249. https://doi.org/10.1016/j.autcon.2018.02.001
  6. Siu, Ming Fung., Lu, M. and AbouRizk, S. (2015). “Zero-One Programming Approach to Determine Optimum Resource Supply under Time-Dependent Resource Constraint.” Journal of Computing in Civil Engineering, ASCE, 10.1061/(ASCE)CP.1943-5487.0000498 , 04015028.
  7. Lau, Sze Chun; Lu, M., Poon, C.S. (2014) “Formalized Approach to Discretize a Continuous Plant in Construction Simulations”, J. Constr. Eng. Manage., ASCE, 2014, 140, 04014032-(1-9). DOI: 10.1061/(ASCE)CO.1943-7862.0000872
  8. Mao, Sheng, Shen, X. and Lu. M. (2014.) “Virtual Laser Target Board for Alignment Control and Machine Guidance in Tunnel-Boring Operations.” Journal of Intelligent and Robotic Systems, J Intell Robot Syst, Springer, (16 pages). DOI 10.1007/s10846-014-0113-y
  9. Soleimanifar, Meimanat Shen, X., Lu, M., Nikolaidis I. (2014). Applying received signal strength based methods for indoor positioning and tracking in construction applications, Canadian Journal of Civil Engineering, 10.1139/cjce-2013-0433 (14 pages)
  10. DAI, Fei, and Lu, M. (2013). "Three-Dimensional Modeling of Site Elements by Analytically Processing Image Data Contained in Site Photos" J. Constr. Eng. Manage., ASCE 139(7), 881-894.
  11. Shen, Xuesong, Lu, M., and Chen, W. (2011). “Tunnel-boring machine positioning during microtunneling operations through integrating automated data collection with real time computing.” Journal of Construction Engineering and Management, ASCE, 137(1), 72-85.
  12. Liang, Xiong, Lu, M., and Zhang, J. P. (2011). “On-site visualization of building component erection enabled by integration of four-dimensional modeling and automated surveying.” Automation in Construction, Elsevier, 20(3), 236-246.
  13. Lu, M, Zhang, Yang, Zhang J., Hu, Z. And Li, J. (2009) “Integration of 4D CAD modeling and 3D animation of operations simulation for visualizing construction of the main stadium for Beijing 2008 Olympic Games”, Canadian Journal of Civil Engineering. 36: 473-479. (Dr Lu served as Co-superviser at Tsinghua University, China)
  14. Lu, M., W. H. Chan, Zhang, J.P. and Cao Ming (2007) “Generic process mapping and simulation methodology for integrating site layout and operations planning in construction”. Journal of Computing in Civil Engineering, ASCE, 21(6), 453-462. (Dr Lu served as Co-superviser at Tsinghua University, China)

Selected publications (thesis-based Master’s students supervision)

  1. Liu, Jiongyang, Ming-Fung Francis Siu, Ming Lu. (2017). "Modular construction system simulation incorporating off-shore fabrication and multi-mode transportation" Proceedings of the 2016 Winter Simulation Conference. T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds. pp: 3269-3280.
  2. Hasan, Monjurul; *Lu, Ming (2017). "Error Quantification and Visualization in Using Sensors to Position Backhoe Excavator" ASCE International Workshop on Computing in Civil Engineering in 2017, Seattle, WA, Jun 25-27, 2017. selected for publication in an ASCE book "Computing in Civil Engineering in 2017: Smart Safety, Sustainability, and Resilience.", Lin, KY, El-Gohery, N, and Tang, P. (Eds) pp 150-157.
  3. Zheng, Chaoyu, Lu M., (2016). “Optimized Reinforcement Detailing Design for Sustainable Construction: Slab Case Study”, Proceedings of International conference on Sustainable Design, Engineering and Construction, Procedia Engineering Vol-145(2016) 1478-1485, 8 pages, Elsevier, USA.
  4. Hu, Xiao-Lin, *Lu, M. and AbouRizk, S. (2014). "BIM-Based Data Mining Approach To Estimating Job Man-Hour Requirements In Structural Steel Fabrication." Proceedings of the 2014 Winter Simulation Conference (A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds.) pp 3399-3410, IEEE, Dec. 11-14, 2014, Savannah, Georgia, United States.
  5. Liu, Chang, Lu M. (2014) “Optimizing Earthmoving Job Planning Based on Evaluation of Temporary Haul Road Networks Design for Mass Earthworks Projects”, J. Constr. Eng. Manage., ASCE, 2014, 140, 04014082-(1-14). DOI: 10.1061/(ASCE)CO.1943-7862.0000940.
  6. Morley, David; Lu M.; AbouRizk, S. (2014) “Identification of Invariant Average Weighted Haul Distance to Simplify Earthmoving Simulation Modeling in Planning Site Grading Operations” " J. Constr. Eng. Manage., ASCE 04014057-(1-11). DOI: 10.1061/(ASCE)CO.1943-7862.0000907.
  7. Wu, Xiao Dong; Lu, M.; Shen, X. (2014) “Computational Approach to As-built Tunnel Invert Survey Based on Processing Real-Time TBM Tracking Data” " Journal of Computing in Civil Engineering, ASCE, 25(3), 232-241. 
  8. Lu, M. and Lam, Hoi-Ching (2009) “Transform schemes applied on non-finish-to-start logical relationships in project network diagrams.” Journal of Construction Engineering and Management, ASCE. 135(9), 863-873.
  9. Chan, Wah Ho., Lu, M. (2008) “Materials Handling System Simulation in Precast Viaduct Construction: Modeling, Analysis, and Implementation.” Journal of Construction Engineering and Management (ASCE) Volume 134, No. 4 (April 2008): 300-310.
  10. Lu, M., Wu, Dapeng., and Zhang, J.P. (2006) “A particle swarm optimization-based approach to tackling simulation optimization of stochastic, large-scale and complex systems”, Lecture Notes in Computer Science, Vol 3930, 528-537. (Dr Lu served as Co-superviser at Tsinghua University, China)
  11. Tang, S. L., Ying, K.C., Anson, M. and Lu, M (2005)”RMCSIM: A simulation model of a ready-mixed concrete plant serving multiple sites using multiple truckmixers” Construction Management and Economics (Impact Factor: 0.8). 01/2005; 23(1):15-31. DOI: 10.1080/0144619032000124661
  12. Lu, M, and Wong, L.C. (2006) “Comparison of two simulation methodologies in modeling construction systems: Manufacturing-oriented PROMODEL vs. construction-oriented SDESA”, Automation in Construction 16 (2007), 86-95.

Software resulting from research

SDESA for operations simulation

In-house development of Simplified Discrete-Event Simulation Approach (SDESA) and SDESA computer simulation platform; Providing a promising alternative to process mapping, simulation and optimization of a real construction system. Supporting undergrad/graduate levels of teaching, facilitates industry training and consultancy and state-of-the-art construction research across universities in Hong Kong, mainland China, U.S., Canada, and Europe. Implementations for engineers to experiment on and, eventually, leading to productive, efficient and economical field operations. SDESA Applications include the following:

  • Operations simulation and visualization for construction of the “Bird’s Nest” - the main stadium for Beijing 2008 Olympic Games 
  • Chan, W. H., Lu, M., Zhang, J. P. (2006). “Attaining cost efficiency in constructing sports facilities for Beijing 2008 Olympics games by use of operations simulation.” Proceedings of the 2006 Winter Simulation Conference, IEEE, 1524-1532, Monterey, Calif., Dec. 2006.
  • Simulation approach to evaluating cost efficiency of selective demolition practices: case of Hong Kong’s Kai Tak airport demolition
  • Lu, M. Lau, S-C, and Poon, C-S. (2009) “Simulation approach to evaluating cost efficiency of selective demolition practices: case of Hong Kong’s Kai Tak airport demolition”, Journal of Construction Engineering and Management, ASCE. 135 (6), 448-457.
  • Materials Handling System Simulation in Precast Viaduct Construction: case of the Hong Kong’s Western Corridor Construction
  • Chan, W.H., Lu, M. (2008) “Materials Handling System Simulation in Precast Viaduct Construction: Modeling, Analysis, and Implementation.” Journal of Construction Engineering and Management (ASCE) Volume 134, No. 4 (April 2008): 300-310.
  • Practical simulation approach to planning Hong Kong’s concrete plant operations: operations simulation, optimization, automated data collection by positioning mixer trucks in highly dense urban areas and building sites
  • Lu, M., Dai, F. and Chen, W. (2007) “Real-time decision support for planning concrete plant operations enabled by integrating vehicle-tracking technology, simulation and optimization algorithms.” Canadian J. of Civil Engineering, 34(8), 912-922
  • Lu, M., M. Anson, S. L. Tang, and Ying, K. C. (2003), “HKCONSIM: A practical simulation approach to planning Hong Kong’s concrete plant operations”, Journal of Construction Engineering and Management, ASCE, 129(5), 547-554.
  • Lu, M., and Lam. H. C. (2009). “Simulation-optimization integrated approach to planning ready mixed concrete production and delivery: validation and applications.” Proceedings of the 2009 Winter Simulation Conference, IEEE, 2593-2604, Austin, US.
  • Lu, M., Lam, H. C. (2005). “Optimized concrete delivery scheduling using combined simulation and genetic algorithms.” Proceedings of the 2005 Winter Simulation Conference, IEEE, 2572-2580, Orlando, Dec. 2005.

S3 for construction project scheduling

  • In-house development of resource-constrained project scheduling and optimization method and computer software (S3)
  • Critical path analysis, project delay analysis under practical constraints such as resource availability and calendars
  • Project scheduling with uncertain activity times: PERT simulation and optimization under resource constraints; 
  • Facilitating Bayesian updating of project duration distributions
  • P2S (Primavera to S3) convertor automatically retrieve project data from P3/P6.
  • Lu, M, Lam, H.C. and Dai, F (2008) “Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization”, Automation in Construction, 17, 670-681.

Sensitive NN (Sensitive Neural Network) is a back propagation NN modeling tool for pattern recognition and feature selection and features the sensitivity analysis function to reveal the rationale of NN’s reasoning. 

  • Lu, M., AbouRizk, S., and Hermann, U. (2001), “Sensitivity analysis of neural networks in spool fabrication productivity studies”, Journal of Computing in Civil Engineering, ASCE, Vol 15(4), 299-308.

PINN (Probability Inference Neural Network) is a classification-prediction combined neural network modeling tool that presents its response in the form of a probability distribution on the potential output range. Thus, a decision can be made for a future scenario by combining the PINN’s recommendation with personal judgment.

  • Lu, M., AbouRizk, S., and Hermann, U.(2000), “Estimating labor productivity using probabilistic inference neural network”, Journal of Computing in Civil Engineering, ASCE, 14/4, 241-248.

Automation systems resulting from research

Integrated intelligent system for tracking, positioning, visualization for microtunneling 

  • In house development of automated system for tunnel boring machine (TBM) positioning and tracking, tunnel alignment control
  • Real-time 3D visualization of TBM, as-designed vs. as-built tunnel sections, underground workspace
  • Applying Artificial Neural Networks and operations simulation to model TBM advance rates in varying ground conditions and support construction planning

Intelligent technology-enabled system for monitoring, simulating, and optimizing Hong Kong’s concrete plant operations (called “HKCONSIM real-time”). 

  • Consisting of integrated positioning and navigation units and wireless data communications networks developed for highly dense urban areas and building construction sites. 
  • The intelligence of the system is powered by GIS, simulation, and optimization engines. 
  • To help attain the highest logistical and operational efficiency in serving all site clients, the reasoning engines behind the digital map analyze the most updated data available and instantaneously suggest the best action plan.

Teaching

Teaching Contributions (Courses developed, offered at U of A)

CIV E 406: Construction Estimating, Planning, and Control

(annual offering; 100 fifth/fourth year students per year)

Course Outline:

Introduction to elements of construction, planning, scheduling, and cost estimating. Familiarization with quantity take-off, estimate preparation, cost recovery, resource allocation, project scheduling, risk analysis, and bid preparation. Prerequisite: CIV E 303.

Contribution:

Dr. Lu has revamped the course materials in 2010-2015. New labs result in some publications as below:

1. Lu, Ming. (2017) “Formalizing a Construction Planning Framework to Facilitate Construction-Centric BIM Education and Practical Application “(15,000 words; peer-reviewed; officially published in summer 2017); 

This is a full chapter of the new ASCE-published Monograph titled "Transforming Engineering Education through Innovative Computer Mediated Learning Technologies"; peer reviewed, total 7 chapters; Dr. Renate Frucher, Standford U and Dr. Ivan Mutis, Illinois Institute of Tech (eds); published by Education Committee, part of the American Society of Civil Engineers (ASCE) Technical Council on Computing and Information Technology.

2. Lu, Ming., Hasan, Tarequl; Hasan, Monjurul. (2017) "Comparative study of UNIFORMAT and MASTERFORMAT for construction cost estimating" CSCE  2017 Annual Conference/International Conference on Construction Specialty "Leadership in Sustainable Infrastructure", Vancouver, Canada, May 31 – June 3, 2017. pp. 186-1 to 186-9.


3. LU, Ming and Liu, Chang. (2014). “Crew Cost and Productivity Performance Benchmarking based on Commercial Cost Estimating Databases”, Construction Research Congress 2014 ©ASCE 2014 1713-1722.


CIV E 601: Analytical Methods for Project Delivery 

Previously called “Project Management” (20-40 graduate students per year)

Course Outline:

This course covers the overview of project management for capital construction projects. Emphasis will be placed on project planning based on the interpretation of engineering designs, constructability, design of construction methods and crews, and design and cost analysis for temporary facilities in construction. A systematic approach consisting of analytical methods for project delivery will be emphasized, encompassing project breakdown structure, work breakdown structure, project network model design, material quantity takeoff, detailed estimating, contingency assessment in bidding, critical path scheduling (including risk analysis, resource allocation and leveling, time-cost trade-off, linear and repetitive scheduling), budgeting, and earned value analysis for project control.

CIV E 607: Productivity Modeling and Analysis

Previously called “Work Improvement Study” (20-40 graduate students per year)

Course Outline:

This course is designed to teach how to define, model, analyze, and improve productivity. It also covers planning for productivity improvement studies, productivity measurement techniques, data analysis and quantitative evaluation, lean concept, human behaviour, quality and safety as factors in productivity improvement; field-ready computer tools for productivity improvement, the roles and impact of automation and robotics in productivity modeling and improvement; opportunities for research and development in pragmatic productivity improvement.