Please wait a minute...
 首页  期刊列表 期刊订阅 开放获取 关于我们
在线预览  |  当期目录  |  过刊浏览  |  热点文章  |  下载排行
Frontiers of Engineering Management    2019, Vol. 6 Issue (3) : 384-394
Proposing a “lean and green” framework for equipment cost analysis in construction
Ming LU(), Nicolas DIAZ, Monjurul HASAN
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
全文: PDF(429 KB)   HTML
导出: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

One limitation of previous productivity-driven research on equipment selection and operation simulation lies in the fact that the green aspects of construction activities have been largely neglected in analysis of cost-efficiency of construction operations. On the other hand, studies attempting to measure greenhouse gas emission due to construction activities have yet to develop a methodology that correlates their findings and implications with construction productivity. In order to address the immediate need for improving the sustainability performance of construction projects, it is imperative for the construction industry to evaluate greenhouse gas emission as a cost factor in construction planning, equipment selection, and cost estimating. In this context, this paper formalizes an integrative framework for equipment cost analysis based on the concepts of lean construction and green construction, aimed to guide the selection of appropriate construction equipment considering exhaust emission and productivity performance at the same time. The framework is elaborated in earthwork construction in order to evaluate the impact of greenhouse gas emission in estimating equipment hourly rates and assessing greenness and sustainability for alternative equipment options.

Keywords green construction      lean construction      equipment      simulation      earthwork construction      sustainability      productivity     
最新录用日期:    在线预览日期:    发布日期: 2019-09-04
Ming LU
Nicolas DIAZ
Monjurul HASAN
Ming LU,Nicolas DIAZ,Monjurul HASAN. Proposing a “lean and green” framework for equipment cost analysis in construction[J]. Front. Eng, 2019, 6(3): 384-394.
网址:     OR
Fig.1  “Lean and green” framework for equipment selection and cost analysis
Fig.2  Typical duo-equipment interactive process model in earthwork construction based on SDESA
1 S AbouRizk, D Hajjar (1998). A framework for applying simulation in the construction industry. Canadian Journal of Civil Engineering, 25(3): 604–617
2 C Ahn, F Peña-Mora, S Lee, C A Arboleda (2013). Consideration of the environmental cost in construction contracting for public works: A+C and A+B+C bidding methods. Journal of Management Engineering, 29(1): 86–94
3 A A Al-Sudairi (2007). Evaluating the effect of construction process characteristics to the applicability Dd of lean principles. Construction Innovation: Information, Process. Management, 7(1): 99–121
4 A Al-Sudairi, J E Diekmann, A D Songer, H M Brown (1999) Simulation of construction processes: Traditional practices versus lean principles. In: Proceedings of 7th Annual Conference of International Group of Lean Construction, 39–50
5 R F Aziz, S M Hafez (2013). Applying lean thinking in construction and performance improvement. Alexandria Engineering Journal, 52(4): 679–695
6 G Ballard, G Howell (1994). Implementing lean construction: Stabilizing work flow. In: Proceedings of 2nd Annual Conference of the International Group for Lean Construction, Santiago, Chile, 101–110
7 K Barati, X Shen (2016). Operational level emissions modelling of on-road construction equipment through field data analysis. Automation in Construction, 72: 338–346
8 D G Carmichael, X Shen, V Peansupap (2019). The relationship between heavy equipment cost efficiency and cleaner production in construction. Journal of Cleaner Production, 211: 521–529
9 Caterpillar (2017).  Caterpillar Performance Handbook. Version No. 47. Peoria, IL: Caterpillar
10 T Cheng, C Feng, Y Chen (2005). A hybrid mechanism for optimizing construction simulation models. Automation in Construction, 14(1): 85–98
11 A S I Conte (2002). Lean construction: From theory to practice. In: Proceedings of 10th Annual Conference of International Group for Lean Construction, Gramado, Brazil, 1–9
12 P Dunlop, S D Smith (2004). Planning, estimation and productivity in the lean concrete pour. Engineering, Construction, and Architectural Management, 11(1): 55–64
13 J M Farrar, S M AbouRizk, X Mao (2004). Generic implementation of lean concepts in simulation models. Lean Construction Journal, 1(1): 1–23
14 J L Fernandez-Solis, V Porwal, S Lavy, A Shafaat, Z K Rybkowski, K Son, N Lagoo (2013). Survey of motivations, benefits, and implementation challenges of last planner system users. Journal of Construction Engineering and Management, 139(4): 354–360
15 H Golzarpoor, V Gonzalez (2013). A green-lean simulation model for assessing environmental and production waste in construction. In: Proceedings of 21th Annual Conference of the International Group for Lean Construction, Fortaleza, Brazil, 885–894
16 H Golzarpoor, V González, M Shahbazpour, M O’Sullivan (2017). An input-output simulation model for assessing production and environmental waste in construction. Journal of Cleaner Production, 143: 1094–1104
17 V González, T Echaveguren (2012). Exploring the environmental modeling of road construction operations using discrete-event simulation. Automation in Construction, 24: 100–110
18 D D Gransberg, E P O’Connor (2015). Major equipment life-cycle cost analysis. St. Paul, MN: Minnesota Department of Transportation Research Services & Library
19 S D Green (1999). The missing arguments of lean construction. Construction Management and Economics, 17(2): 133–137
20 M Hasan, M Lu (2017). Error quantification and visualization in using sensors to position backhoe excavator. In: ASCE International Workshop on Computing in Civil Engineering, Seattle, Washington, USA, 150–157
21 B Heidari, L C Marr (2015). Real-time emissions from construction equipment compared with model predictions. Journal of the Air & Waste Management Association, 65(2): 115–125
22 G Howell, G Ballard (1998). Implementing lean construction: Understanding and action. In: Proceedings of 6th Annual Conference of International Group for Lean Construction, Guaruja, Brazil
23 J E Hummer, I Arocho, W Rasdori (2017). Approach to assessing tradeoffs between construction equipment fleet emissions and cost. Journal of Construction Engineering and Management, 143(5): 1–10
24 J Kim, C Koo, C J Kim, T Hong, H S Park (2015). Integrated CO2, cost, and schedule management system for building construction projects using the earned value management theory. Journal of Cleaner Production, 103: 275–285
25 C H Ko, N F Chung (2014). Lean design process. Journal of Construction Engineering and Management, 140(6): 04014011
26 L Koskela, G Howell, G Ballard, I Tommelein (2002). Foundations of lean construction. In: Best R, de Valence G, eds. Design and Construction: Building in Value. Oxford: Butterworth-Heinemann, Elsevier
27 L Koskela (1992). Application of the New Production Philosophy to Construction. Technical Report No. 72. Center for Integrated Facility Engineering, Stanford University, CA, USA
28 A M Law (2015). Simulation Modeling and Analysis. Boston: McGraw-Hill
29 P Lewis, W Rasdorf (2017). Fuel use and pollutant emissions taxonomy for heavy duty diesel construction equipment. Journal of Management Engineering, 33(2): 04016038
30 W Li, X G Wang (2016). Innovations on management of sustainable construction in a large earthwork project: An Australian case research. Procedia Engineering, 145: 677–684
31 M Lu (2003). Simplified discrete-event simulation approach for construction simulation.  Journal of Construction Engineering and Management,  129(5): 537–546
32 M Lu, M Hasan (2018). Productivity improvement in operating autonomous plants subject to random breakdowns in construction. In: Proceedings of 2018 Winter Simulation Conference (WSC), IEEE, Gothenburg, Sweden, 3885–3896
33 M Lu, L C Wong (2005). Comparing PROMODEL and SDESA in modeling construction operations. In: Proceedings of the 37th Winter Simulation Conference, Orlando, FL, USA, 1524–1532
34 M Lu, H C Lam, F Dai (2008). Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization. Automation in Construction, 17(6): 670–681
35 X Mao, X Zhang (2008). Construction process reengineering by integrating lean principles and computer simulation techniques. Journal of Construction Engineering and Management, 134(5): 371–381
36 M Marzouk, O Moselhi (2003). Object-oriented simulation model for earthmoving operations. Journal of Construction Engineering and Management, 129(2): 173–181
37 C J M Miller, G A Packham, B C Thomas (2002). Harmonization between main contractors and subcontractors: A prerequisite for lean construction? Journal of Construction Research, 03(01): 67–82
38 D Morley, M Lu, T Joseph (2013). In search of the ideal truck-excavator combination.  In: Proceedings of 30th International Symposium on Automation and Robotics in Construction, Montreal, Quebec, Canada
39 O Moselhi, A Alshibani (2009). Optimization of earthmoving operations in heavy civil engineering projects. Journal of Construction Engineering and Management, 135(10): 948–954
40 R L Peurifoy, G D Oberlender (2013). Estimating Construction Costs (6th Edition). New York: McGraw-Hill Higher Education
41 W Rasdorf, C Frey, P Lewis, K Kim, S H Pang, S Abolhassani (2010). Field procedures for real-world measurements of emissions from diesel construction vehicles. Journal of Infrastructure Systems, 16(3): 216–225
42 S Rosenbaum, M Toledo, V González (2012). Green-lean approach for assessing environmental and production waste in construction. In: Proceedings of 20th Annual Conference of International Group for Lean Construction, San Diego, USA
43 R Sacks, M Radosavljevic, R Barak (2010). Requirements for building information modeling based lean production management systems for construction. Automation in Construction, 19(5): 641–655
44 O Salem, J Solomon, A Genaidy, I Minkarah (2006). Lean construction: From theory to implementation. Journal of Management Engineering, 22(4): 168–175
45 G Shang, P L Sui (2014). Lean Construction Management: The Toyota Way. Singapore: Springer
46 J J Shi (1999). A neural network based system for predicting earthmoving production. Construction Management and Economics, 17(4): 463–471
47 H R Thomas, M J Horman, U E L de Souza, I Zavřski (2002). Reducing variability to improve performance as a lean construction principle. Journal of Construction Engineering and Management, 128(2): 144–154
48 D I Tommelein (1998). Pull-driven scheduling for pipe-spool installation: Simulation of lean construction technique. Journal of Construction Engineering and Management, 124(4): 279–288
49 I D Tommelein, A E Y Li (1999). Just-in-time concrete delivery: Mapping alternatives for vertical supply chain integration. In: Proceedings of 7th Annual Conference of International Group for Lean Construction, Berkeley, California, USA, 97–108
50 A G Uriarte, A H C Ng, M U Moris, J Oscarson (2015). Lean, simulation and optimization: A win-win combination. In: Proceedings of the 2015 Winter Simulation Conference, IEEE, Huntington Beach, CA, USA, 2227–2238
51 U.S. Environmental Protection Agency (EPA) (2009). Potential for reducing greenhouse gas emissions in the construction sector. Washington D.C.: U.S. Environmental Protection Agency
52 Vision (2017). Winter-wise solution to cold weather construction challenges.  Visions, Publication of Graham Construction
53 T Wang, J Wang, P Wu, J Wang, Q He, X Wang (2017). Estimating the environmental costs and benefits of demolition waste using life cycle assessment and willingness-to-pay: A case study in Shenzhen. Journal of Cleaner Production, 172: 14‒26
54 World Commission on Environment, and Development (1986). Our Common Future, a Report of World Commission on Environment and Development. Oxford: Oxford University Press
55 J K Yates (2014). Design and construction for sustainable industrial construction. Journal of Construction Engineering and Management, 140(4): B4014005
56 C Yi, M Lu (2018). A Simulation-based Earthmoving Fleet Optimization Platform (SEFOP) for truck/excavator selection in rough grading project. In: Proceedings of 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany, 956–962
57 J Yoon, J Kim, S Suh, S Suh (2014). Spatial factors affecting the loading efficiency of excavators. Automation in Construction, 48: 97–106
58 H Zhang, C M Tam, H Li, J J Shi (2006). Particle swarm optimization-supported simulation for construction operations. Journal of Construction Engineering and Management, 132(12): 1267–1274
59 M Zhang, T Cao, X Zhao (2017). Applying sensor-based technology to improve construction safety management. Sensors (Switzerland), 17(8): 1841
No related articles found!
Full text



版权所有 © 2015 高等教育出版社.
电话: 010-58556848 (技术); 010-58556485 (订阅) E-mail: