Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Building Research Journal

4 Issues per year

Open Access
See all formats and pricing
More options …

Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

Mojtaba Maghrebi
  • Corresponding author
  • The University of New South Wales (UNSW), School of Civil and Environmental Engineering, Sydney, Australia
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Claude Sammut
  • The University of New South Wales (UNSW), School of Computer Science Engineering Sydney, Australia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Travis S. Waller
  • The University of New South Wales (UNSW), School of Civil and Environmental Engineering, Sydney, Australia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-06-17 | DOI: https://doi.org/10.2478/brj-2014-0001


Being able to precisely predict the duration of concrete operations can help construction managers to organize sites and machineries more efficiently, especially when there is limited space for equipment on site. Currently there is no theoretical method for estimating the duration of the concrete pouring process. Normally, the maximum capacity of pumping facilities on construction sites is not used, and concrete pumps are idle for a considerable time as a result of the arrival of concrete trucks being delayed. In the light of this issue, this paper considers the supply chain parameters of Ready Mixed Concrete (RMC) as a means of solving this problem. Artificial Neural Network (ANN) is hired for modelling/predicting the productivity of a concrete operation. The proposed model is tested with a real database of an RMC in the Sydney metropolitan area that has 17 depots and around 200 trucks. Results show that there is an improvement in the achieved results when these are compared to the results of relevant studies that only considered the construction parameters for predicting the productivity of concrete operations

Keywords: RMC; productivity; supply chain


  • [1] Abourizk, S. M. and Wales, R. J. (1997), “Combined discrete-event/continuous simulation for project planning”, Journal of construction engineering and management 123, 11-20.Google Scholar

  • [2] Armstrong , T. (2013), “The Global Cement Report”, In: 10TH (ed.), International Cement Review.Google Scholar

  • [3] Asbach, L., Dorndorf, U. and Pesch, E. (2009), “Analysis, modeling and solution of the concrete delivery problem”, European Journal of Operational Research 193, 820-835.CrossrefGoogle Scholar

  • [4] Attalla, M. and Hegazy, T. (2003), “Predicting cost deviation in reconstruction projects: Artificial neural network versus regression”, Journal of Construction Engineering and Management-Asce 129, 405-411.Google Scholar

  • [5] Borcherding, J. D. and Alarcon, L. F. (1991), “Quantitative effects on construction productivity”, Constr. Law. 11, 1.Google Scholar

  • [6] Brega, J. R. F., Soria, M. H. A., Marar, J. F. and Sementille, A. C. (1998), An intelligent system for pavement management. In: Rogers, S. K., Fogel, D. B., Bezdek, J. C. and Bosacchi, B. (eds.) Applications and Science of Computational Intelligence. Bellingham: Spie-Int Soc Optical Engineering.Google Scholar

  • [7] Cao, M., Lu, M. and Zhang, J.-P. (2004), “Concrete plant operations optimization using combined simulation and genetic algorithms”, Machine Learning and Cybernetics, Proceedings of 2004 International Conference on, 2004. IEEE, 4204-4209.Google Scholar

  • [8] Chao, L.-C. and Skibniewski, M. J. (1994), “Estimating construction productivity: Neuralnetwork- based approach”, Journal of Computing in Civil Engineering 8, 234-251Google Scholar

  • [9] Chehayeb, A., Al-Hussein, M. and Flynn, P. (2007), “An integrated methodology for collecting, classifying, and analyzing Canadian construction court cases”, Canadian Journal of Civil Engineering 34, 177-188.CrossrefGoogle Scholar

  • [10] Chen, J. H. and Hsu, S. C. (2007), “Hybrid ANN-CBR model for disputed change orders in construction projects”, Automation in Construction 17, 56-64.CrossrefGoogle Scholar

  • [11] Cheng, M. Y., Tsai, H. C. and Liu, C. L. (2009) “Artificial intelligence approaches to achieve strategic control over project cash flows”, Automation in Construction 18, 386-393.CrossrefGoogle Scholar

  • [12] Cheng, T. M. and Yan, R. Z. (2009), “Integrating messy genetic algorithms and simulation to optimize resource utilization”, Computer‐Aided Civil and Infrastructure Engineering 24, 401-415.CrossrefGoogle Scholar

  • [13] Council, W. B. (2009), World Business Council for Sustainable Development/The Cement Sustainability Initiative, Cement Industry Energy and CO2 Performance: Getting the Numbers Right.Google Scholar

  • [14] Crawford, P. and VOGL, B. (2006), “Measuring productivity in the construction industry”, Building Research & Information 34, 208-219.Google Scholar

  • [15] Damtoft, J., Lukasik, J., Herfort, D., Sorrentino, D. and Gartner, E. (2008), “Sustainable development and climate change initiatives”, Cement and concrete research 38, 115-127.Google Scholar

  • [16] Darrat, A. F. and Zhong, M. (2000) “On Testing the Random‐Walk Hypothesis: A Model‐ Comparison Approach”, Financial Review 35, 105-124.CrossrefGoogle Scholar

  • [17] Dunlop, P. and Smith, S. D. (2004), “Planning, estimation and productivity in the lean concrete pour”, Engineering, Construction and Architectural Management 11, 55-64.Google Scholar

  • [18] Fast, M. and Palme, T. (2010), “Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant”, Energy 35, 1114-1120.CrossrefGoogle Scholar

  • [19] Feng, C.-W., Cheng, T.-M. and Wu, H.-T. (2004), “Optimizing the schedule of dispatching RMC trucks through genetic algorithms”, Automation in Construction 13, 327-340.CrossrefGoogle Scholar

  • [20] Feng, C.-W. and Wu, H.-T. (2006), “Integrating fmGA and CYCLONE to optimize the schedule of dispatching RMC trucks”, Automation in Construction 15, 186-199.CrossrefGoogle Scholar

  • [21] Flood, I. and Christophilos, P. (1996), “Modeling construction processes using artificial neural networks”, Automation in construction 4, 307-320.Google Scholar

  • [22] Garcia, J., Lozano, S., Smith, K., Kwok, T. and Villa, G. (2002), “Coordinated scheduling of production and delivery from multiple plants and with time windows using genetic algorithms, Neural Information Processing”, ICONIP'02, Proceedings of the 9th International Conference on, IEEE, 1153-1158.Google Scholar

  • [23] Graham, L. D., Forbes, D. R. and Smith, S. D. (2006), “Modeling the ready mixed concrete delivery system with neural networks”, Automation in construction 15, 656-663.Google Scholar

  • [24] Humphreys, K. and Mahasenan, M. (2002) Towards a sustainable cement industry, Climate change, sub-study 8, World Business Council for Sustainable Development. Google Scholar

  • [25] Imbabi, M. S., Carrigan, C. and Mckenna, S. (2012), “Trends and developments in green cement and concrete technology”, International Journal of Sustainable Built Environment 1, 194-216.Google Scholar

  • [26] Jin, X. H. and Zhang, G. M. (2011), ”Modelling optimal risk allocation in PPP projects using artificial neural networks”, International Journal of Project Management 29, 591-603.Google Scholar

  • [27] Kale, S. and Karaman, E. A. (2011), “Evaluating the Knowledge Management Practices of Construction Firms by Using Importance-Comparative Performance Analysis Maps”, Journal of Construction Engineering and Management-Asce 137, 1142-1152.Google Scholar

  • [28] Kartam, N. (1996), “Neural Network-Spreadsheet Integration for Earth‐Moving Operations”, Computer‐Aided Civil and Infrastructure Engineering 11, 283-288.Google Scholar

  • [29] Kim, D. Y., Han, S. H., Kim, H. and Park, H. (2009), “Structuring the prediction model of project performance for international construction projects: A comparative analysis”, Expert Systems with Applications 36, 1961-1971.Google Scholar

  • [30] Kim, S. (2013), “Hybrid forecasting system based on case-based reasoning and analytic hierarchy process for cost estimation”, Journal of Civil Engineering and Management 19, 86-96.Google Scholar

  • [31] Ko, C. H. and Cheng, M. Y. (2007), “Dynamic prediction of project success using artificial intelligence”, Journal of Construction Engineering and Management-Asce 133, 316-324.Google Scholar

  • [32] Kosmatka, S. H., Panarese, W. C., Allen, G. E. and Cumming, S. (2002), Design and control of concrete mixtures, Portland Cement Association Skokie, Ill.Google Scholar

  • [33] Levenberg, K. (1944) “A method for the solution of certain problems in least squares”, Quarterly of applied mathematics 2, 164-168.Google Scholar

  • [34] Lin, P.-C., Wang, J., Huang, S.-H. and Wang, Y.-T. (2010), “Dispatching ready mixed concrete trucks under demand postponement and weight limit regulation”, Automation in Construction 19, 798-807.CrossrefGoogle Scholar

  • [35] Liu, M. and Ling, Y. Y. (2003), “Using fuzzy neural network approach to estimate contractors' markup”, Building and Environment 38, 1303-1308.Google Scholar

  • [36] Lu, M., Abourizk, S. and Hermann, U. H. (2000), “Estimating labor productivity using probability inference neural network”, Journal of Computing in Civil Engineering 14, 241-248.Google Scholar

  • [37] 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 Journal of Civil Engineering 34, 912-922.CrossrefGoogle Scholar

  • [38] Lu, M. and Lam, H.-C. (2005), “Optimized concrete delivery scheduling using combined simulation and genetic algorithms”, Proceedings of the 37th conference on Winter simulation, Winter Simulation Conference, 2572-2580.Google Scholar

  • [39] Lu, M., Wu, D.-P. and Zhang, J.-P. (2006), A particle swarm optimization-based approach to tackling simulation optimization of stochastic, large-scale and complex systems, Advances in Machine Learning and Cybernetics, Springer. Google Scholar

  • [40] Mahasenan, N., Smith, S., Humphreys, K. and Kaya, Y. (2003), “The cement industry and global climate change: current and potential future cement industry CO2 emissions”, Greenhouse Gas Control Technologies-6th International Conference, Oxford: Pergamon, 995-1000.Google Scholar

  • [41] Marquardt, D. W. (1963), “An algorithm for least-squares estimation of nonlinear parameters”, Journal of the Society for Industrial & Applied Mathematics 11, 431-441.Google Scholar

  • [42] McCulloch, W. S. and Pitts, W. (1943), “A logical calculus of the ideas immanent in nervous activity”, Bulletin of mathematical biology 5, 115-133.Google Scholar

  • [43] Mehta, P. K. (2009), “Global concrete industry sustainability”, Concrete international 31, 4.Google Scholar

  • [44] Ming, L. and Hoi-Ching, L. (2009), “Simulation-optimization integrated approach to planning ready mixed concrete production and delivery: Validation and applications”, Simulation Conference (WSC), Proceedings of the 2009 Winter, 13-16 Dec. 2009. 2593-2604.Google Scholar

  • [45] Moselhi, O., Hegazy, T. and Fazio, P. (1991), “Neural networks as tools in construction”, Journal of Construction Engineering and Management-Asce 117, 606-625.Google Scholar

  • [46] Naso, D., Surico, M., Turchiano, B. and Kaymak, U. (2007), “Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete”, European Journal of Operational Research 177, 2069-2099.CrossrefGoogle Scholar

  • [47] Nissen, S. and Nemerson, E. (2000), Fast artificial neural network library, Available at leenissen. dk/fann/html/files/fann-h. html.Google Scholar

  • [48] Pan, L., Liya, W., Xihai, D. and Xiang, G. (2010), “Scheduling of dispatching Ready Mixed Concrete trucks trough Discrete Particle Swarm Optimization”, Systems Man and Cybernetics (SMC), IEEE International Conference on, 10-13 Oct. 2010. 4086-4090.Google Scholar

  • [49] Payr, F. and Schmid, V. (2009), “Optimizing Deliveries of Ready-Mixed Concrete”, Logistics and Industrial Informatics, LINDI 2009, 2nd International, IEEE, 1-6.Google Scholar

  • [50] Portas, J. and Abourizk, S. (1997), “Neural network model for estimating construction productivity”, Journal of construction engineering and management 123, 399-410.Google Scholar

  • [51] Qin, Q., Wang, Q.-G., Li, J. and Ge, S. S. (2013), “Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market”, Journal of Intelligent Learning Systems and Applications 5, 1-10.Google Scholar

  • [52] Rosenthal, E. (2007), “Cement industry is at center of climate change debate”, New York Times, 26.Google Scholar

  • [53] Savin, D., Alkass, S. and Fazio, P. (1996), “Construction resource leveling using neural networks”, Canadian Journal of Civil Engineering 23, 917-925.CrossrefGoogle Scholar

  • [54] Shi, H. W. and Li, W. Q. (2008), Application of PSO-based Neural Network in Quality Assessment of Construction Project, Los Alamitos, Ieee Computer Soc.Google Scholar

  • [55] Shi, J. J. (1999), “A neural network based system for predicting earthmoving production”, Construction Management & Economics 17, 463-471. Google Scholar

  • [56] Silva, C. A., Faria, J. M., Abrantes, P., Sousa, J. M. C., Surico, M. and Naso, D. (2005), “Concrete Delivery using a combination of GA and ACO. Decision and Control and European Control Conference”, CDC-ECC '05. 44th IEEE Conference on, 12-15 Dec. 2005, 7633-7638.Google Scholar

  • [57] Sonmez, R. and Rowings, J. E. (1998), “Construction labor productivity modeling with neural networks”, Journal of Construction Engineering and Management 124, 498-504.Google Scholar

  • [58] Srichandum, S. and Rujirayanyong, T. (2010) “Production scheduling for dispatching ready mixed concrete trucks using bee colony optimization”, Am. J. Engg. & Applied Sci 3, 823-830.Google Scholar

  • [59] Szu, H., Sheng, Y. and Chen, J. (1992), “Wavelet transform as a bank of the matched filters”, Applied optics 31, 3267-3277.PubMedCrossrefGoogle Scholar

  • [60] Thomas, H. R. (1991), “Labor productivity and work sampling: The bottom line”, Journal of Construction Engineering and Management 117, 423-444.Google Scholar

  • [61] Thomas, H. R. and Daily, J. (1983), “Crew performance measurement via activity sampling”, Journal of Construction Engineering and Management 109, 309-320.Google Scholar

  • [62] Thomas, H. R., Guevara, J. M. and Gustenhoven, C. T. (1984), “Improving productivity estimates by work sampling”, Journal of construction engineering and management 110, 178-188.Google Scholar

  • [63] Thomas, H. R. and Sakarcan, A. S. (1994), “Forecasting labor productivity using factor model”, Journal of Construction Engineering and Management 120, 228-239.Google Scholar

  • [64] Worrell, E., Price, L., Martin, N., Hendriks, C. and Meida, L. O. (2001), “Carbon dioxide emissions from the global cement industry 1”, Annual Review of Energy and the Environment 26, 303-329.Google Scholar

  • [65] Wu, D.-P., Lu, M. and Zhang, J.-P. (2005), “Efficient optimization procedures for stochastic simulation systems. Machine Learning and Cybernetics”, Proceedings of 2005 International Conference on, 2005. IEEE, 2895-2900.Google Scholar

  • [66] Yan, S., Lai, W. and Chen, M. (2008), “Production scheduling and truck dispatching of ready mixed concrete”, Transportation Research Part E: Logistics and Transportation Review 44, 164-179.Google Scholar

  • [67] Yan, S., Lin, H. and Jiang, X. (2012), “A planning model with a solution algorithm for ready mixed concrete production and truck dispatching under stochastic travel times”, Engineering Optimization 44, 427-447.CrossrefGoogle Scholar

  • [68] Zahraee, S. M., Hatami, M., Mohd Yusof, N., Mohd Rohani, J. and Ziaei, F. (2013), “Combined Use of Design of Experiment and Computer Simulation for Resources Level Determination in Concrete Pouring Process”, Jurnal Teknologi 64.Google Scholar

  • [69] Zor, I., Guzel, A., Ozyurek, H. and Erdogan, E. (2012), “Abnormal Effects of Weekdays on Forecasting Stock Prices by Neural Networks”, European Journal of Social Sciences 29, 244-259. Google Scholar

About the article

Published Online: 2014-06-17

Published in Print: 2014-06-01

Citation Information: Building Research Journal, Volume 61, Issue 1, Pages 1–14, ISSN (Online) 1339-682X, DOI: https://doi.org/10.2478/brj-2014-0001.

Export Citation

© 2014. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Associate Professor Monty Sutrisna and Profes, Mojtaba Maghrebi, Claude Sammut, and S. Travis Waller
Engineering, Construction and Architectural Management, 2015, Volume 22, Number 5, Page 573

Comments (0)

Please log in or register to comment.
Log in