Advertisement

Neural Computing and Applications

, Volume 30, Issue 8, pp 2489–2497 | Cite as

Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine

  • Min-Yuan Cheng
  • Nhat-Duc Hoang
Original Article

Abstract

Diaphragm wall is a widely used method for excavating the foundation of buildings. Foundation construction plays a prominent role since it is a predecessor of many other activities. Therefore, estimating the diaphragm wall duration at the planning phase is a practical need of the project manager. This research proposes an artificial intelligence method, named as FLSVM, for predicting duration of diaphragm wall construction. The proposed method is developed by a fusion of the least squares support vector machine (LS-SVM) and the firefly algorithm (FA). LS-SVM is used to generalize the mapping function between the diaphragm wall duration and its influencing factors. Meanwhile, the FA method is employed for finding an appropriate set of the LS-SVM tuning parameters. Historical cases collected from construction projects in Taiwan are utilized to establish and verify the new approach. Based on the experimental results, the FLSVM model can deliver accurate forecasts since it has achieved a comparatively low prediction deviation which is <10%. This fact proves that the proposed approach is very helpful for construction managers when planning the schedule of diaphragm wall-related projects.

Keywords

Artificial intelligence Construction management Diaphragm wall construction Firefly algorithm Least squares support vector machine Schedule estimation 

References

  1. 1.
    Tamaro M, Pamukcu S, Lopez P (1993) Prediction of structural slurry wall behavior. In: Proceedings of the third international conference on case histories in geotechnical engineering, St. Louis, Missouri, June 1–4, 1993Google Scholar
  2. 2.
    Ou C-Y (2006) Deep excavation—theory and practice. Taylor & Francis Group, LondonGoogle Scholar
  3. 3.
    Yang M, Chen S, Chen S (2006) Innovative central opening strut system for foundation excavation. J Constr Eng Manag 132:58–66CrossRefGoogle Scholar
  4. 4.
    Dzeng R-J, Pan N-F (2006) Learning heuristics for determining slurry wall panel lengths. Autom Constr 15:303–313CrossRefGoogle Scholar
  5. 5.
    Xanthakos PP (1993) Slurry walls as structural systems. Mcgraw-Hill, New YorkGoogle Scholar
  6. 6.
    El-Razek M (1999) New method for construction of diaphragm walls. J Constr Eng Manag 125:233–241CrossRefGoogle Scholar
  7. 7.
    Sears K, Sears G, Clough R (2008) Construction project management: a practical guide to field construction management, 5th edn. Wiley, HobokenGoogle Scholar
  8. 8.
    Mubarak S (2010) Construction project scheduling and control. Wiley, HobokenCrossRefGoogle Scholar
  9. 9.
    Cheng M-Y, Hoang N-D (2013) Interval estimation of construction cost at completion using least squares support vector machine. J Civ Eng Manag 20:223–236CrossRefGoogle Scholar
  10. 10.
    Suykens J, Gestel JV, Brabanter JD, Moor BD, Vandewalle J (2002) Least square support vector machines. World Scientific Publishing Co. Pte. Ltd., SingaporeCrossRefGoogle Scholar
  11. 11.
    Tien Bui D, Tuan TA, Hoang N-D, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2016) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, pp 1–12Google Scholar
  12. 12.
    Zhang N (2016) Extended least squares support vector machines for ordinal regression. Neural Comput Appl 27:1497–1509CrossRefGoogle Scholar
  13. 13.
    Cheng M-Y, Hoang N-D, Limanto L, Wu Y-W (2014) A novel hybrid intelligent approach for contractor default status prediction. Knowl Based Syst 71:314–321CrossRefGoogle Scholar
  14. 14.
    Cheng M, Hoang N (2014) Risk score inference for bridge maintenance project using evolutionary fuzzy least squares support vector machine. J Comput Civ Eng ASCE 28:04014003CrossRefGoogle Scholar
  15. 15.
    Karaboga D, Kaya E (2016) An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Appl Soft Comput 49:423–436CrossRefGoogle Scholar
  16. 16.
    Shabani MO, Rahimipour MR, Tofigh AA, Davami P (2015) Refined microstructure of compo cast nanocomposites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques. Neural Comput Appl 26:899–909CrossRefGoogle Scholar
  17. 17.
    Nazari A, Sanjayan JG (2015) Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine. Ceram Int 41:12164–12177CrossRefGoogle Scholar
  18. 18.
    Kang F, Li J (2016) Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng 30:04015040CrossRefGoogle Scholar
  19. 19.
    Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-inspired Comput 2:78–84CrossRefGoogle Scholar
  20. 20.
    Yang XS (2008) Firefly algorithm. Luniver Press, BristolGoogle Scholar
  21. 21.
    Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46CrossRefGoogle Scholar
  22. 22.
    Yang X-S (2014) Nature-inspired optimization algorithms. Elsevier, OxfordzbMATHGoogle Scholar
  23. 23.
    Baykasoğlu A, Ozsoydan FB (2014) An improved firefly algorithm for solving dynamic multidimensional knapsack problems. Expert Syst Appl 41:3712–3725CrossRefGoogle Scholar
  24. 24.
    Amiri B, Hossain L, Crawford JW, Wigand RT (2013) Community detection in complex networks: multi-objective enhanced firefly algorithm. Knowl Based Syst 46:1–11CrossRefGoogle Scholar
  25. 25.
    Yang JB (1997) An integrated knowledge acquisition and problem solving model for experience-oriented problems in construction management. Ph.D. dissertation, National Central University, TaiwanGoogle Scholar
  26. 26.
    Bishop C (2006) Pattern recognition and machine learning. Springer, SingaporezbMATHGoogle Scholar
  27. 27.
    Hoang N-D, Tien Bui D, Liao K-W (2016) Groutability estimation of grouting processes with cement grouts using Differential Flower Pollination Optimized Support Vector Machine. Appl Soft Comput 45:173–186CrossRefGoogle Scholar
  28. 28.
    Cheng M-Y, Tsai H-C, Ko C-H, Chang W-T (2008) Evolutionary fuzzy neural inference system for decision making in geotechnical engineering. J Comput Civ Eng 22:272–280CrossRefGoogle Scholar
  29. 29.
    Wang L, Zeng Y, Chen T (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst Appl 42:855–863CrossRefGoogle Scholar
  30. 30.
    Ekonomou L, Christodoulou CA, Mladenov V (2016) An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters. Neural Comput Appl 27:1143–1148CrossRefGoogle Scholar
  31. 31.
    Cao MS, Pan LX, Gao YF, Novák D, Ding ZC, Lehký D, Li XL (2015) Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Comput Appl 1–8. doi: 10.1007/s00521-015-2132-4 CrossRefGoogle Scholar
  32. 32.
    Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Environ 74:103–116CrossRefGoogle Scholar
  33. 33.
    Chou J-S, Chiu C-K, Farfoura M, Al-Taharwa I (2011) Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J Comput Civ Eng 25:242–253CrossRefGoogle Scholar
  34. 34.
    Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533CrossRefGoogle Scholar
  35. 35.
    Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993CrossRefGoogle Scholar
  36. 36.
    Riedmiller M, Braun H (1993) A direct adaptive method for faster back-propagation learning: The RPROP algorithm. In: Proceedings of the IEEE international conference on neural networks, San Francisco, CA, 28 Mar 1993–01 Apr 1993, pp 586–591Google Scholar
  37. 37.
    Arlot S (2010) A survey of cross-validation procedures for model selection. Stat Surv 4(2010):40–79MathSciNetCrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Civil and Construction EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Institute of Research and Development, Faculty of Civil EngineeringDuy Tan UniversityDanangVietnam

Personalised recommendations