[1]祁神军,张云波,丁烈云.建设工程项目工序的LS-SVM工期预测模型[J].华侨大学学报(自然科学版),2010,31(5):562-565.[doi:10.11830/ISSN.1000-5013.2010.05.0562]
　QI Shen-jun,ZHANG Yun-bo,DING Lie-yun.Forecast Model of Activity Duration Based on LS-SVM in Construction Engineering Project[J].Journal of Huaqiao University(Natural Science),2010,31(5):562-565.[doi:10.11830/ISSN.1000-5013.2010.05.0562]
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建设工程项目工序的LS-SVM工期预测模型()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第31卷
期数:
2010年第5期
页码:
562-565
栏目:
出版日期:
2010-09-20

文章信息/Info

Title:
Forecast Model of Activity Duration Based on LS-SVM in Construction Engineering Project
文章编号:
1000-5013(2010)05-0562-04
作者:
祁神军; 张云波; 丁烈云
华侨大学土木工程学院; 华中科技大学土木工程与力学学院
Author(s):
QI Shen-jun1; 2;  ZHANG Yun-bo1;  DING Lie-yun2
1.College of Civil Engineering, Huaqiao University, Quanzhou 362021, China; 2.College of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
关键词:
建设工程项目;  最小二乘向量机;  工序;  工期预测
Keywords:
construction engineering project;  least square support vector machine;  activity;  activity duration forecast
分类号:
TU722
DOI:
10.11830/ISSN.1000-5013.2010.05.0562
文献标志码:
A
摘要:
鉴于传统工期预测的模糊性和随机性,分析影响工程项目工期的因素及参数的获取方式.采用最小二乘支持向量机(LS-SVM)构建建设工程项目工序工期的预测模型,并用工程实例论证方法的有效性.结果表明,对类似工程或者同一工程项目的类似工序的进度执行状况进行学习,采用LS-SVM的工期预测模型预测即将开展的工程项目的工序工期,符合实际工期控制的要求.与基于BP神经网络工期预测模型对比分析,LS-SVM的工期预测模型的预测误差更小,平均训练时间更短,网络总误差更小.
Abstract:
Referring to fuzziness and randomness for activity duration forecast of construction engineering project by traditional ways,the influence factors of activity duration are analyzed,and parameter calculation is also proposed.A forecast model of activity duration based on the least square support vector machine(LS-SVM) is set up,and the analysis of a subway case confirms validity of this model.The model is trained by the schedule execution situation of the activities in other similar projects or the similar activities in the same project,the activity duration simulated by the model conforms with the request of schedule controlling.In the forecast model of activity duration based on LS-SVM,the prediction and network total errors is less,training time is shorter than the ones in he forecast model based on BP.

参考文献/References:

[1] 陆歆弘. 模糊假言推理确定施工工期 [J]. 基建优化, 1999(5):18-21.
[2] 何飞, 张贵平. 工程施工工期概率:模糊估算法 [J]. 陕西建筑, 2000(2):42-45.
[3] SANDERS S R, THOMAS H R. Factors affecting masonry-labor productivity [J]. Journal of Construction Engineering and Management ASCE, 1991(4):626-644.
[4] THOMAS H R, MATHEWS C T, WARD J G. Learning curve models of construction productivity [J]. Journal of Construction Engineering and Management ASCE, 1986(2):245-258.
[5] OVARARIN N, POPESCU C M. Field factors affecting masonry productivity [A]. Pittsburgh, PA, USA, 2001.91-100.
[6] BOTERO L F, ALVAREZ M E. Last planner:An advance in planning and controlling construction projects:Case study in the city of Medellin [A]. Porto Alegre, 2005.1-9.
[7] SUVKENS J A K, VUNDEWULLE J. LeusL squares support vector machine classifiers [J]. Neural Processing Letters, 1999(3):293-300.
[8] SALEM O, SOLOMON J, GENAIDYETAL A. Site implementation and assessment of lean construction techniques [J]. Lean Construction Journal, 2005(2):1-21.
[9] 张云波, 胡云昌. 人工神经网络的建设工期定额地域分类 [J]. 华侨大学学报(自然科学版), 2004(3):270-274.doi:10.3969/j.issn.1000-5013.2004.03.012.
[10] 祁神军, 丁烈云, 骆汉宾. 大型工程项目工序工期精准预测方法研究 [J]. 重庆建筑大学学报, 2007(6):141-144.doi:10.3969/j.issn.1674-4764.2007.06.032.
[11] 宋志宇, 李俊杰. 最小二乘支持向量机在大坝变形预测中的应用 [J]. 水电能源科学, 2006(6):49-52.doi:10.3969/j.issn.1000-7709.2006.06.015.
[12] 辛治运, 顾明. 基于最小二乘支持向量机的复杂金融时间序列预测 [J]. 清华大学学报（自然科学版）, 2008(7):1147-1149.doi:10.3321/j.issn:1000-0054.2008.07.021.

备注/Memo

备注/Memo:
国务院侨办科研基金资助项目(08QZR06); 华侨大学高层次人才科研启动项目(07BS404)
更新日期/Last Update: 2014-03-23