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Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

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Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

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The authors declare no conflicts of interest.

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*Journal of Building Construction and Planning Research*,

**1**, 1-7. doi: 10.4236/jbcpr.2013.11001.

[1] | G.-H. Kim, J.-E. Yoon, S.-H. An, H-H. Cho and K.-I. Kang, “Neural Network Model Incorporating a Genetic Algorithm in Estimating Construction Costs,” Building and Environment, Vol. 39, No. 11, 2004, pp. 1333-1340. doi:10.1016/j.buildenv.2004.03.009 |

[2] | G.-H. Kim and S.-H. An, “A Study on the Correlation between Selection Methods of Input Variables and Number of Data in Estimating Accuracy; Cost Estimating Using Neural Networks in Apartment Housing Projects,” Journal of the Architectural Institute of Korea, Vol. 23, No. 4, 2007, pp. 129-137. |

[3] | G.-H. Kim, S.-H. An and K.-I. Kang, “Comparison of Construction Cost Estimating Models Based on Regression Analysis, Neural Networks, and Case-Based Reasoning,” Building and Environment, Vol. 39, No. 10, 2004, pp. 1235-1242. doi:10.1016/j.buildenv.2004.02.013 |

[4] | H.-G. Cho, K.-G. Kim, J.-Y. Kim and G.-H. Kim, “A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network in Elementary School Project,” Journal of the Korea Institute of Building Construction, Vol. 13, No. 1, 2013, pp. 66-74. doi:10.5345/JKIBC.2013.13.1.066 |

[5] | S.-H. An and K.-I. Kang, “A Study on Predicting Construction Cost of Apartment Housing Using Experts’ Knowledge at the Early Stage of Projects,” Journal of the Architectural Institute of Korea, Vol. 21, No. 6, 2005, pp. 81-88. |

[6] | U.-Y. Park and G.-H. Kim, “A Study on Predicting Construction Cost of Apartment Housing Projects Based on Support Vector Regression at the Early Project Stage,” Journal of the Architectural Institute of Korea, Vol. 23, No. 4, 2007, pp. 165-172. |

[7] | S. Singh, “Cost Model for Reinforced Concrete Beam and Slab Structures in Building,” Journal of Construction Engineering and Management, Vol. 116, No. 1, 1990, pp. 54-67. doi:10.1061/(ASCE)0733-9364(1990)116:1(54) |

[8] | K.-D. Kim, “A Study on the Development of the Cost Model for the Domestic Apartment House,” Ph.D. Thesis, Seoul National University, Seoul, 1991. |

[9] | I.-S. Choi, S.-H. Hong, C.-B. Son and S.-C. Ko, “A Study on the Prediction Model of Construction Cost in HighRise Office Building of SRC Type,” Journal of the Architectural Institute of Korea, Vol. 15, No. 7, 1999, pp. 143-151. |

[10] | R. Mckim, “Neural Network Application to Cost Engineering,” Cost Engineering, Vol. 35, No. 7, 1993, pp. 3135. |

[11] | I.-C. Yeh, “Quantity Estimating of Building with Logarithm-Neuron Networks,” Journal of Construction Engineering and Management, Vol. 124, No. 5, 1998, pp. 374-380. doi:10.1061/(ASCE)0733-9364(1998)124:5(374) |

[12] | J. Bode, “Neural Networks for Cost Estimating: Simulation and Pilot Application,” International Journal of Production Research, Vol. 38, No. 6, 2000, pp. 123-154. doi:10.1080/002075400188825 |

[13] | S.-K. Kim and I.-W. Koo, “A Neural Network Cost Model for Office Buildings,” Journal of the Architectural Institute of Korea, Vol. 16, No. 9, 2000, pp. 59-67. |

[14] | X. Wu and L. Cai, “Application of RS-SVM in Construction Project Cost Forecasting,” Proceedings of the 4th International Conference on Wireless Communication, Networking and Mobile Computing, Dalian, 12-14 October 2008, pp. 1-4. |

[15] | M.-Y. Cheng and Y.-W. Wu, “Construction Conceptual Cost Estimates Using Support Vector Machine,” Proceedings of the 22nd International Symposium on Automation and Robotics in Construction ISARC 2005, Ferrara, 11-14 September 2005, pp. 1-5. |

[16] | S.-H. An, K.-I. Kang, M.-Y. Cho and H.-H. Cho, “Application of Support Vector Machines in Assessing Conceptual Cost Estimates,” Journal of Computing in Civil Engineering, Vol. 21, No. 4, 2007, pp. 259-264. doi:10.1061/(ASCE)0887-3801(2007)21:4(259) |

[17] | W. Yunna, “Application of a Case-Based Reasoning Method in Estimating the Power Grid Project Cost,” Proceedings of the 4th International Conference on Wireless Communication, Networking and Mobile Computing, Dalian, 12-14 October 2008, pp. 1-5. |

[18] | S.-H. Ji, M. Park and H.-S. Lee, “Case Adaptation Method of Case-Based Reasoning for Construction Cost Estimation in Korea,” Journal of Construction Engineering and Management, Vol. 138, No. 1, 2007, pp. 43-52. doi:10.1061/(ASCE)CO.1943-7862.0000409 |

[19] | S.-H. An, G.-H. Kim and K.-I. Kang, “A Case-Based Reasoning Cost Estimating Model Using Experience by Analytic Hierarchy Process,” Building and Environment, Vol. 42, No. 7, 2007, pp. 2573-2579. doi:10.1016/j.buildenv.2006.06.007 |

[20] | J. Garza and K. Rouhana, “Neural Network versus Parameter-Based Application,” Cost Engineering, Vol. 37, No. 2, 1995, pp. 14-18. |

[21] | W.-Y. Park, J.-H. Cha and K.-I. Kang, “A Neural Network Cost Model for Apartment Housing Projects in the Initial Stage,” Journal of the Architectural Institute of Korea, Vol. 18, No. 7, 2002, pp. 155-162. |

[22] | G.-H. Kim, S.-H. An and H.-K. Cho, “Comparison of the Accuracy between Cost Prediction Models Based on Neural Network and Genetic Algorithm: Focused on Apartment Housing Project Cost,” Journal of the Architectural Institute of Korea, Vol. 23, No. 3, 2006, pp. 111-118. |

[23] | J.-M. Shin and G.-H. Kim, “A Study on Predicting Construction Cost of Educational Building Project at Early Stage Using Support Vector Machine Technique,” Journal of Korean Institute of Educational Environment, Vol. 11, No. 3, 2012, pp. 46-54. |

[24] | H. Adeli and M. Wu, “Regularization Neural Network for Construction Cost Estimation,” Journal of Construction Engineering and Management, Vol. 124, No. 1, 1998, pp. 18-24. doi:10.1061/(ASCE)0733-9364(1998)124:1(18) |

[25] | J. Bode, “Neural Networks for Cost Estimation,” Cost Engineering, Vol. 40, No. 1, 1998, pp. 25-30. |

[26] | A. E. Smith and A. K. Mason, “Cost Estimating Predictive Modeling: Regression versus Neural Network,” Engineering Economist, Vol. 42, No. 2, 1997, pp. 137-161. doi:10.1080/00137919708903174 |

[27] | R. M. Skitmore and B. R. T. Patchell, “Development in Contract Price Forecasting and Bidding Techniques,” In: M. Skitmore and V. Marston, Eds., Cost Modelling, E& FN Spon, London, 1990, pp. 53-84. |

[28] | R. Creese and L. Li, “Cost Estimation of Timber Bridge Using Neural Networks,” Cost Engineering, Vol. 37, No. 5, 1995, pp. 17-22. |

[29] | H. Li, “Neural Networks for Construction Cost Estimation,” Building Research and Information, Vol. 23, No. 5, 1995, pp. 279-284. doi:10.1080/09613219508727476 |

[30] | S. Deng and T.-H. Yeh, “Applying Least Squares Support Vector Machines to the Airframe Wing-Box Structural Design Cost Estimation,” Expert Systems with Applications, Vol. 37, No. 12, 2010, pp. 8417-8423. doi:10.1016/j.eswa.2010.05.038 |

[31] | T. Hegazy, P. Fazio and O. Moselhi, “Developing Practical Neural Network Application Using Back-Propagation,” Computer-Aided Civil and Infrastructure Engineering, Vol. 9, No. 2, 1994, pp. 145-159. doi:10.1111/j.1467-8667.1994.tb00369.x |

[32] | V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer, London, 1999. |

[33] | A. J. Smola and B. Schölkopf, “A Tutorial on Support Vector Regression,” Statistics and Computing, Vol. 14, No. 3, 2004, pp. 199-222. doi:10.1023/B:STCO.0000035301.49549.88 |

[34] | C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167. doi:10.1023/A:1009715923555 |

[35] | M.-Y. Cheng, H.-S. Peng, Y.-W. Wu and T.-L. Chen, “Estimate at Completion for Construction Projects Using Evolutionary Support Vector Machine Inference Model,” Automation in Construction, Vol. 19, No. 5, 2010, pp. 619-629. doi:10.1016/j.autcon.2010.02.008 |

[36] | Y. Shin, D.-W. Kim, J.-Y. Kim, K.-I. Kang, M.-Y. Cho and H.-H. Cho, “Application of Adaboost to the Retaining Wall Method Selection in Construction,” Journal of Computing in Civil Engineering, Vol. 23, No. 3, 2009, pp. 188-192. doi:10.1061/(ASCE)CP.1943-5487.0000001 |

[37] | P. R. Kumar and V. Ravi, “Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques—A Review,” European Journal of Operational Research, Vol. 180, No. 1, 2007, pp. 1-28. doi:10.1016/j.ejor.2006.08.043 |

[38] | M. Skitmore, “Early Stage Construction Price Forecasting: A Review of Performance,” Occasional Paper, Royal Institute of Chartered Surveyors, London, 1991. |

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