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Exploring Productivity of Concrete Truck for Multistory Building Projects Using Discrete Event Simulation

  • Construction Management
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Abstract

Concrete pouring activity is essential for the schedule and quality of the structural work construction. In practice, the process of concrete pouring is frequently congested and interrupted due to many unforeseeable reasons. The primary purpose of this study is to explore the productivity of concrete trucks for multistory building projects. The interview technique and work sampling method have been employed to collect the necessary data. Based on the literature review and experts’ opinions, twenty-five factors affecting the productivity of pouring concrete have been found and discussed. Among them, there are seven factors identified as different from previous studies. Through two case studies of the hospital project, the actual average productivity of one concrete truck used to pour concrete into columns and walls is 0.184 m3/min by using a truck-mounted pump and 0.087 m3/min by using a tower crane. These productivities have been then determined based on discrete event simulation (DES). The simulation results indicated that the simulated productivity is higher than the actual productivity of approximately 16% and 13% for truck-mounted pump and tower crane, respectively. It is concluded that DES is a handy simulation tool for construction operations before the period of implementation. Based on the relationship between events of the process of concrete pouring, two relevant solutions have been proposed to enhance the productivity of concrete trucks. The results of this study may help practitioners manage the concreting activities in their projects with higher productivity.

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References

  • AbouRizk SM, Hajjar D (1998) A framework for applying simulation in construction. Canadian Journal of Civil Engineering 25(3):604–617, DOI: https://doi.org/10.1139/197-123

    Google Scholar 

  • AbouRizk S, Halpin D, Mohamed Y, Hermann U (2011) Research in modeling and simulation for improving construction engineering operations. Journal of Construction Engineering and Management 137(10):843–852, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000288

    Google Scholar 

  • Afifi M, Al-Hussein M, Abourizk S, Fotouh A, Bouferguene A (2016) Discrete and continuous simulation approach to optimize the productivity of modular construction element. Proceedings of 33rd international symposium on automation and robotics in construction, July 18–21, Auburn, AL, USA

  • Alazzaz F, Whyte A (2015) Linking employee empowerment with productivity in off-site construction. Engineering, Construction and Architectural Management 22(1):21–37, DOI: https://doi.org/10.1108/ECAM-09-2013-0083

    Google Scholar 

  • Al-Hussein M, Niaz MA, Yu H, Kim H (2006) Integrating 3D visualization and simulation for tower crane operations on construction sites. Automation in Construction 15(5):554–562, DOI: https://doi.org/10.1016/j.autcon.2005.07.007

    Google Scholar 

  • Alvanchi A, Azimi R, Lee S, AbouRizk SM, Zubick P (2012) Off-site construction planning using discrete event simulation. Journal of Architectural Engineering 18(2):114–122, DOI: https://doi.org/10.1061/(ASCE)AE.1943-5568.0000055

    Google Scholar 

  • Alzraiee H, Moselhi O, Zayed T (2012) A hybrid framework for modeling construction operations using discrete event simulation and system dynamics. Construction research congress, May 21–23, West Lafayette, IN, USA, DOI: https://doi.org/10.1061/9780784412329.107

  • Aziz Z, Qasim RM, Wajdi S (2017) Improving productivity of road surfacing operations using value stream mapping and discrete event simulation. Construction Innovation 17(3):294–323, DOI: https://doi.org/10.1108/CI-11-2016-0058

    Google Scholar 

  • Borg L, Song H-S (2015) Quality change and implications for productivity development: housing construction in Sweden 1990–2010. Journal of Construction Engineering and Management 141(1):05014014, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000928

    Google Scholar 

  • Botín JA, Campbell AN, Guzmán R (2015) A discrete-event simulation tool for real-time management of pre-production development fleets in a block-caving project. International Journal of Mining, Reclamation and Environment 29(5):347–356

    Google Scholar 

  • Bügler M, Dori G, Borrmann A (2013) SWAP-based process schedule optimization using discrete-event simulation. Proceedings of 13th international conference on construction applications of virtual reality, October 30–31, London, UK

  • Chalker M, Loosemore M (2016) Trust and productivity in Australian construction projects: A subcontractor perspective. Engineering, Construction and Architectural Management 23(2):192–210, DOI: https://doi.org/10.1108/ECAM-06-2015-0090

    Google Scholar 

  • Chen H-M, Huang P-H (2013) 3D AR-based modeling for discrete-event simulation of transport operations in construction. Automation in Construction 33:123–136, DOI: https://doi.org/10.1016/j.autcon.2012.09.015

    Google Scholar 

  • Chia FC, Skitmore M, Runeson G, Bridge A (2012) An analysis of construction productivity in Malaysia. Construction Management and Economics 30(12):1055–1069, DOI: https://doi.org/10.1080/01446193.2012.711910

    Google Scholar 

  • Chia FC, Skitmore M, Runeson Q Bridge A (2014) Economic development and construction productivity in Malaysia. Construction Management and Economics 32(9):874–887, DOI: https://doi.org/10.1080/01446193.2014.938086

    Google Scholar 

  • Dang DM (2017) Construction of concrete activity. Construction Publisher, Hanoi, Vietnam, 25–33 (in Vietnamese)

    Google Scholar 

  • Doloi H (2008) Application of AHP in improving construction productivity from a management perspective. Construction Management and Economics 26(8):841–854

    Google Scholar 

  • Durdyev S, Ismail S (2012) Pareto analysis of on-site productivity constraints and improvement techniques in construction industry. Scientific Research and Essays 7(7):824–833, DOI: https://doi.org/10.5897/SRE12.005

    Google Scholar 

  • El-Gohary KM, Aziz RF, Abdel-Khalek HA (2017) Engineering approach using ANN to improve and predict construction labor productivity under different influences. Journal of Construction Engineering and Management 143(8):04017045, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001340

    Google Scholar 

  • Erikshammar J, Lu W, Stehn L, Olofsson T (2013) Discrete event simulation enhanced value stream mapping: An industrialized construction case study. Lean Construction Journal 10:47–65

    Google Scholar 

  • Golizadeh H, Sadeghifam AN, Aadal H, Majid MZA (2016) Automated tool for predicting duration of construction activities in tropical countries. KSCE Journal of Civil Engineering 20(1):12–22, DOI: https://doi.org/10.1007/s12205-015-0263-x

    Google Scholar 

  • González V, Echaveguren T (2012) Exploring the environmental modeling of road construction operations using discrete-event simulation. Automation in Construction 24:100–110, DOI: https://doi.org/10.1016/j.autcon.2012.02.011

    Google Scholar 

  • Gurmu AT, Aibinu AA (2017) Construction equipment management practices for improving labor productivity in multistory building construction projects. Journal of Construction Engineering and Management 143(10), DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001384

  • Han S, Halpin DW (2005) The use of simulation for productivity estimation based on multiple regression analysis. Proceedings of 37th conference on winter simulation, December 4–7, Orlando, FL, USA

  • Hassan MM, Gruber S (2008) Application of discrete-event simulation to study the paving operation of asphalt concrete. Construction Innovation 8(1):7–22, DOI: https://doi.org/10.1108/14714170810846495

    Google Scholar 

  • Heravi G, Eslamdoost E (2015) Applying artificial neural networks for measuring and predicting construction-labor productivity. Journal of Construction Engineering and Management 141(10), DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001006

  • Hu X, Liu C (2016) Energy productivity and total-factor productivity in the Australian construction industry. Architectural Science Review 59(5):432–444, DOI: https://doi.org/10.1080/00038628.2015.1038692

    Google Scholar 

  • Hughes R, Thorpe D (2014) A review of enabling factors in construction industry productivity in an Australian environment. Construction Innovation 14(2):210–228, DOI: https://doi.org/10.1108/CI-03-2013-0016

    Google Scholar 

  • Hwang B-G, Soh CK (2013) Trade-level productivity measurement: Critical challenges and solutions. Journal of Construction Engineering and Management 139(11), DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000761

  • Ioannou PG, Martinez JC (1996) Comparison of construction alternatives using matched simulation experiments. Journal of Construction Engineering and Management 122(3):231–241, DOI: https://doi.org/10.1061/(ASCE)0733-9364(1996)122:3(231)

    Google Scholar 

  • Islam MA, Khadem MMRK (2013) Productivity determinants in Oman construction industry. International Journal of Productivity and Quality Management 12(4):426–448, DOI: https://doi.org/10.1504/IJPQM.2013.056736

    Google Scholar 

  • Jarkas AM (2015) Factors influencing labour productivity in Bahrain’s construction industry. International Journal of Construction Management 15(1):94–108, DOI: https://doi.org/10.1080/15623599.2015.1012143

    Google Scholar 

  • Jarkas AM, Horner RMW (2015) Creating a baseline for labour productivity of reinforced concrete building construction in Kuwait. Construction Management and Economics 33(8):625–639, DOI: https://doi.org/10.1080/01446193.2015.1085651

    Google Scholar 

  • Jarkas AM, Kadri CY, Younes JH (2012) A survey of factors influencing the productivity of construction operatives in the State of Qatar. International Journal of Construction Management 12(3):1–23, DOI: https://doi.org/10.1080/15623599.2012.10773192

    Google Scholar 

  • Jiradamkerng W (2012) Evaluation of EZStrobe simulation system as a tool in productivity analysis — A case study: Precast concrete hollow-core slab installation. Engineering Journal 17(2):75–84, DOI: https://doi.org/10.4186/ej.2013.17.2.75

    Google Scholar 

  • Kamat VR, Martinez JC (2001) Visualizing simulated construction operations in 3D. Journal of Computing in Civil Engineering 15(4):329–337, DOI: https://doi.org/10.1061/(ASCE)0887-3801(2001)15:4(329)

    Google Scholar 

  • Kapelko M, Horta IM, Camanho AS, Oude Lansink A (2015) Measurement of input-specific productivity growth with an application to the construction industry in Spain and Portugal. International Journal of Production Economics 166:64–71, DOI: https://doi.org/10.1016/j.ijpe.2015.03.030

    Google Scholar 

  • Khanh HD, Kim SY (2014) Determining labor productivity diagram in high-rise building using straight-line model. KSCE Journal of Civil Engineering 18(5):898–908, DOI: https://doi.org/10.1007/s12205-014-0521-3

    Google Scholar 

  • Kisi KP, Mani N, Rojas EM, Foster ET (2017) Optimal productivity in labor-intensive construction operations: Pilot study. Journal of Construction Engineering and Management 143(3):04016107, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001257

    Google Scholar 

  • König M (2011) Robust construction scheduling using discrete-event simulation. Proceedings of 2011 ASCE international workshop on computing in civil engineering, June 19–22, Miami, FL, USA

  • Labban R, AbouRizk S, Haddad Z, Elsersy A (2013) A discrete event simulation model of asphalt paving operations. Proceedings of 2013 winter simulations conference, December 8–11, Washington DC, USA

  • Le PVB (2016) A study on Discrete Event Simulation for construction and industrial projects for improving the effectiveness. MSc Thesis, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam (in Vietnamses)

  • Lee J, Park Y-J, Choi C-H, Han C-H (2017) BIM-assisted labor productivity measurement method for structural formwork. Automation in Construction 84:121–132, DOI: https://doi.org/10.1016/j.autcon.2017.08.009

    Google Scholar 

  • Li X, Chow KH, Zhu Y, Lin Y (2016) Evaluating the impacts of high-temperature outdoor working environments on construction labor productivity in China: A case study of rebar workers. Building and Environment 95:42–52, DOI: https://doi.org/10.1016/j.buildenv.2015.09.005

    Google Scholar 

  • Li Y, Liu C (2012) Labour productivity measurement with variable returns to scale in Australia’s construction industry. Architectural Science Review 55(2):110–118, DOI: https://doi.org/10.1080/00038628.2012.677587

    Google Scholar 

  • Liao P-C, Thomas SR, O’Brien WJ, Dai J, Mulva SP, Kim K (2012) Benchmarking project level engineering productivity. Journal of Civil Engineering and Management 18(2):235–244, DOI: https://doi.org/10.3846/13923730.2012.671284

    Google Scholar 

  • Liu H, Altaf MS, Lei Z, Lu M, Al-Hussein M (2015) Automated production planning in panelized construction enabled by integrating discrete-event simulation and BIM. Proceedings of 5th international/11th construction specialty conference, June 8–10, Vancouver, Canada

  • Liu M, Ballard G (2008) Improving labor productivity through production control. Proceedings of 16th annual conference of the international group for lean construction, July 16–18, Manchester, UK

  • Loera I, Espinosa G, Enríquez C, Rodriguez J (2013) Productivity in construction and industrial maintenance. Procedia Engineering 63:947–955, DOI: https://doi.org/10.1016/j.proeng.2013.08.274

    Google Scholar 

  • Long ND, Ogunlana S, Quang T, Lam KC (2004) Large construction projects in developing countries: A case study from Vietnam. International Journal of Project Management 22(7):553–561, DOI: https://doi.org/10.1016/j.ijproman.2004.03.004

    Google Scholar 

  • Loosemore M (2014) Improving construction productivity: A subcontractor’s perspective. Engineering, Construction and Architectural Management 21(3):245–260, DOI: https://doi.org/10.1108/ECAM-05-2013-0043

    Google Scholar 

  • Lu M (2003) Simplified discrete-event simulation approach for construction simulation. Journal of Construction Engineering and Management 129(5):537–546, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2003)129:5(537)

    Google Scholar 

  • Lu M, Chan W-H (2004) Modeling concurrent operational interruptions in construction activities with Simplified Discrete Event Simulation Approach (SDESA). Proceedings of 36th conference on winter simulation, December 5–8, Washington DC, USA

  • Lu M, Lam H-C, Dai F (2008) Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization. Automation in Construction 17(6):670–681, DOI: https://doi.org/10.1016/j.autcon.2007.11.004

    Google Scholar 

  • Lu W, Olofsson T (2014) Building information modeling and discrete event simulation: Towards an integrated framework. Automation in Construction 44:73–83, DOI: https://doi.org/10.1016/j.autcon.2014.04.001

    Google Scholar 

  • Martinez JC (2001) EZStrobe: General-purpose simulation system based on activity cycle diagrams. Proceedings of 2001 winter simulation conference, December 9–12, Piscataway, NJ, USA

  • Martinez CJ (2010) Methodology for conducting discrete-event simulation studies in construction engineering and management. Journal of Construction Engineering and Management 136(1):3–16, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000087

    Google Scholar 

  • Marzouk M, Moselhi O (2003) Object-oriented simulation model for earthmoving operations. Journal of Construction Engineering and Management 129(2):173–181, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2003)129:2(173)

    Google Scholar 

  • Moon S, Forlani J, Wang X, Tam V (2016) Productivity study of the scaffolding operations in liquefied natural gas plant construction: Ichthys project in Darwin, Northern Territory, Australia. Journal of Professional Issues in Engineering Education and Practice 142(4):04016008, DOI: https://doi.org/10.1061/(ASCE)EI.1943-5541.0000287

    Google Scholar 

  • Moselhi O, Khan Z (2012) Significance ranking of parameters impacting construction labour productivity. Construction Innovation 12(3):272–296, DOI: https://doi.org/10.1108/14714171211244541

    Google Scholar 

  • Mostafa S, Chileshe N, Abdelhamid T (2016) Lean and agile integration within offsite construction using discrete event simulation: A systematic literature review. Construction Innovation 16(4):483–525, DOI: https://doi.org/10.1108/CI-09-2014-0043

    Google Scholar 

  • Nasir H, Ahmed H, Haas C, Goodrum PM (2014) An analysis of construction productivity differences between Canada and the United States. Construction Management and Economics 32(6):595–607, DOI: https://doi.org/10.1080/01446193.2013.848995

    Google Scholar 

  • Nasir H, Haas CT, Rankin JH, Fayek AR, Forgues D, Ruwanpura J (2012) Development and implementation of a benchmarking and metrics program for construction performance and productivity improvement. Canadian Journal of Civil Engineering 39:957–967, DOI: https://doi.org/10.1139/12012-030

    Google Scholar 

  • Nguyen TT (2016) A study on the integration of Building Information Modeling into Discrete Event Simulation for construction project management. MSc Thesis, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam (in Vietnamese)

  • Nguyen LD, Nguyen HT (2013) Relationship between building floor and construction labor productivity: A case of structural work. Engineering, Construction and Architectural Management 20(6):563–575, DOI: https://doi.org/10.1108/ECAM-03-2012-0034

    Google Scholar 

  • Peña-Mora F, Han S, Lee SH, Park M (2008) Strategic-operational construction management: Hybrid system dynamics and discrete event approach. Journal of Construction Engineering and Management 134(9):701–710, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:9(701)

    Google Scholar 

  • Rekapalli PV, Martinez JC (2011) Discrete-event simulation-based virtual reality environments for construction operations: Technology introduction. Journal of Construction Engineering and Management 137(3):214–224, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000270

    Google Scholar 

  • Rustom RN, Yahia A (2007) Estimating productivity using simulation: A case study of Gaza beach embankment protection project. Construction Innovation 7(2):167–186, DOI: https://doi.org/10.1108/14714170710738531

    Google Scholar 

  • Sacks R, Gurevich U, Belaciano B (2015) Hybrid discrete event simulation and virtual reality experimental setup for construction management research. Journal of Computing in Civil Engineering 29(1):04014029, DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000366

    Google Scholar 

  • Sezer AA, Bröchner J (2014) The construction productivity debate and the measurement of service qualities. Construction Management and Economics 32(6):565–574, DOI: https://doi.org/10.1108/14714170710738531

    Google Scholar 

  • Shaheen AA, Fayek AR, AbouRizk SM (2009) Methodology for integrating fuzzy expert systems and discrete event simulation in construction engineering. Canadian Journal of Civil Engineering 36(9):1478–1490, DOI: https://doi.org/10.1139/L09-091

    Google Scholar 

  • Sheikh A, Lakshmipathy M, Prakash A (2016) Application of queuing theory for effective equipment utilization and maximization of productivity in construction management. International Journal of Applied Engineering Research 11(8):5664–5672

    Google Scholar 

  • Song L, AbouRizk SM (2008) Measuring and modeling labor productivity using historical data. Journal of Construction Engineering and Management 134(10):786–794, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:10(786)

    Google Scholar 

  • Sveikauskas L, Rowe S, Mildenberger J, Price J, Young A (2016) Productivity growth in construction. Journal of Construction Engineering and Management 142(10), DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001138

  • TCVN-4453 (1995) Monolithic concrete and reinforced concrete structure — Codes for construction, check and acceptance. Construction Standard, Ministry of Construction, Hanoi, Vietnam (in Vietnamese)

  • Teo EAL, Ofori G, Tjandra IK, Kim H (2015) The potential of Building Information Modelling (BIM) for improving productivity in Singapore construction. Proceedings of 31st annual ARCOM conference, Association of Researchers in Construction Management, September 7–9, Lincoln, UK

  • Thomas HR (2015) Benchmarking construction labor productivity. Practice Periodical on Structural Design and Construction 20(4):04014048, DOI: https://doi.org/10.1061/(ASCE)SC.1943-5576.0000141

    Google Scholar 

  • Thomas HR, Maloney WF, Horner RMW, Smith GR, Handa VK, Sanders SR (1990) Modeling construction labor productivity. Journal of Construction Engineering and Management 116(4):705–726, DOI: https://doi.org/10.1061/(ASCE)0733-9364(1990)116:4(705)

    Google Scholar 

  • Tommelein ID (1997) Discrete-event simulation of lean construction processes. Proceedings of 5th annual conference of the international group for lean construction, July 16–17, Gold Coast, Australia

  • Tommelein ID (1999) Lean construction experiments using discrete-event simulation: Techniques and tools for process re-engineering? International Journal of Computer-Integrated Design and Construction 1(2):53–63

    Google Scholar 

  • Tsehayae AA, Fayek AR (2016) System model for analyzing construction labor productivity. Construction Innovation 16(2):203–228, DOI: https://doi.org/10.1108/CI-07-2015-0040

    Google Scholar 

  • Turner CJ, Hutabarat W, Oyekan J, Tiwar A (2016) Discrete event simulation and virtual reality use in industry: New opportunities and future trends. IEEE Transactions on Human-Machine Systems, 46(6):882–894, DOI: https://doi.org/10.1109/THMS.2016.2596099

    Google Scholar 

  • Vereen SC, Rasdorf W, Hummer J (2016) Development and comparative analysis of construction industry labor productivity metrics. Journal of Construction Engineering and Management 142(7):04016020, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001112

    Google Scholar 

  • Vidalakis C, Tookey JE, Sommerville J (2013) Demand uncertainty in construction supply chains: A discrete event simulation study. Journal of the Operational Research Society 64(8):1194–1204, DOI: https://doi.org/10.1057/jors.2012.156

    Google Scholar 

  • Vogl B, Abdel-Wahab M (2015) Measuring the construction industry’s productivity performance: Critique of international productivity comparisons at industry level. Journal of Construction Engineering and Management 141(4):04014085, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000944

    Google Scholar 

  • Wang X, Chen Y, Liu B, Shen Y, Sun H (2013) A total factor productivity measure for the construction industry and analysis of its spatial difference: A case study in China. Construction Management and Economics 31(10):1059–1071, DOI: https://doi.org/10.1080/01446193.2013.

    Google Scholar 

  • Wikipedia (2019) Kolmogorov-Smirnov test. Wikipedia, Retrieved August 3, 2020, https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnovtest

  • Yang J-B, Huang K-M, Lee C-H, Chiu C-T (2014) Incorporating lost productivity calculation into delay analysis for construction projects. KSCE Journal of Civil Engineering 18(3):380–388, DOI: https://doi.org/10.1007/s12205-014-0128-8

    Google Scholar 

  • Yi W, Chan APC (2014) Critical review of labor productivity research in construction journals. Journal of Management in Engineering 30(2):214–225, DOI: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000194

    Google Scholar 

  • Zhang H, Li H, Tam CM (2004) Fuzzy discrete-event simulation for modeling uncertain activity duration. Engineering, Construction and Architectural Management 11(6):426–437, DOI: https://doi.org/10.1108/09699980410570184

    Google Scholar 

  • Zhang H, Tam CM, Li H (2005) Modeling uncertain activity duration by fuzzy number and discrete-event simulation. European Journal of Operational Research 164(3):715–729, DOI: https://doi.org/10.1016/j.ejor.2004.01.035

    MATH  Google Scholar 

  • Zhang H, Tam CM, Shi JJ (2002) Simulation-based methodology for project scheduling. Construction Management and Economics 20(8):667–678, DOI: https://doi.org/10.1080/0144619022000014088

    Google Scholar 

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Khanh, H.D., Kim, SY. Exploring Productivity of Concrete Truck for Multistory Building Projects Using Discrete Event Simulation. KSCE J Civ Eng 24, 3531–3545 (2020). https://doi.org/10.1007/s12205-020-1389-z

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