装配式建筑安全文明施工费RS-LSSVM预测方法
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摘要
为准确测算装配式建筑安全文明施工费,开发一种基于粗糙集(RS)-最小二乘支持向量机(LSSVM)模型的预测方法。根据装配式建筑作业空间并行多维且以吊装施工为主的特点,分析影响费用的主要因素并通过RS属性约简算法确定其测算因子;引入LSSVM,构建装配式建筑项目安全文明施工费测算模型,给出计算方法以及模型流程;以某城市群部分装配式项目的相关数据进行模型学习训练和仿真测算,以此为例完成实证检验和分析。结果表明:在样本数据较少、指标成多维非线性关系的情况下,用该方法测算所得结果与实际情况符合较好(平均相对误差为4.92%),比BP模型和回归分析等2种传统方法(11.78%和17.67%)测算结果更准确,效率更高。
To get accurate prediction of the safety-civilized measure cost for fabricated building project,a method based on the combination of RS and LSSVM was developed. For developing the method,the measurement factors were ascertained by attribute reduction algorithm in RS according to the features of fabricated building project. A LSSVM was introduced and a RS-LSSVM model was built. The method based on RS-LSSVM was applied to a number of projects in pilot urban agglomeration as an example. The data on these projects were input into the model for training and simulation to verify the method. As the case study shows,under the condition of small-sample case and multi-dimensional nonlinear factors,in comparison with the conventional methods such as the multivariant linear regression and BP neural network method,the proposed method works more efficiently, and can give calculation results more closely conforming to the reality.
To get accurate prediction of the safety-civilized measure cost for fabricated building project,a method based on the combination of RS and LSSVM was developed. For developing the method,the measurement factors were ascertained by attribute reduction algorithm in RS according to the features of fabricated building project. A LSSVM was introduced and a RS-LSSVM model was built. The method based on RS-LSSVM was applied to a number of projects in pilot urban agglomeration as an example. The data on these projects were input into the model for training and simulation to verify the method. As the case study shows,under the condition of small-sample case and multi-dimensional nonlinear factors,in comparison with the conventional methods such as the multivariant linear regression and BP neural network method,the proposed method works more efficiently, and can give calculation results more closely conforming to the reality.
引文
[1]中共中央国务院.国务院办公厅关于促进建筑业持续健康发展的意见[L].2017-02-24.
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[19]温廷新,陈晓宇,杨红玉,等.基于优化Bagging-LSSVM模型的冲击地压预测[J].中国安全科学学报,2017,27(6):121-126.WEN Tingxin,CHEN Xiaoyu,YANG Hongyu,et al.Research on prediction of rock burst based on optimized BaggingLSSVM model[J].Journal of Safety Science and Technology,2017,27(6):121-126.
[20]张晓晖,朱家元,张恒喜.基于LS-SVM的小样本费用智能预测[J].计算机工程与应用,2004,40(27):203-204,214.ZHANG Xiaohui,ZHU Jiayuan,ZHANG Hengxi.Cost intelligent prediction with few observations using LS-SVM[J].Computer Engineering and Applications,2004,40(27):203-204,214.
[2]住房和城乡建设部住宅产业化促进中心.装配式建筑政策与国内外发展[M].北京:中国建筑工业出版社,2016:2-21.
[3]李彦婕,杨丽琼,黄霞.2007—2017我国建筑施工安全事故统计分析[J].科学技术创新,2017(22):132-133.LI Yanjie,YANG Liqiong,HUANG Xia.Statistical analysis on safety accidents of building construction from 2007 to2017 in China[J].Scientific and Technological Innovation,2017(22):132-133.
[4]SHIN Y S,KIM G H.Study on spending for safety management cost in Korean construction sites[J].Applied Mechanics&Materials,2015,752/753:1 228-1 231.
[5]PELLICER E,CARVAJAL G I,RUBIO M C,et al.A method to estimate occupational health and safety costs in construction projects[J].KSCE Journal of Civil Engineering,2014,18(7):1 955-1 965.
[6]MA Yanhong,ZHAO Qiuhong,XI Menghao.Decision-makings in safety investment:an opportunity cost perspective[J].Safety Science,2016,83:31-39.
[7]FENG Yingbin.Effect of safety investments on safety performance of building projects[J].Safety Science,2013,59:28-45.
[8]IDORO G I.Effect of mechanization on occupational health and safety performance in the Nigerian construction industry[J].Journal of Construction in Developing Countries,2011,16(2):27-45.
[9]李英攀,刘名强,王芳,等.装配式建筑项目安全绩效云模型评价方法[J].中国安全科学学报,2017,27(6):115-120.LI Yingpan,LIU Mingqiang,WANG Fang,et al.Safety performance assessment of fabricated building project based on cloud model[J].China Safety Science Journal,2017,27(6):115-120.
[10]赵文虎.建筑装配式住宅施工安全风险管理探析[J].建筑安全,2017(5):13-15.ZHAO Wenhu.Analysis on fabricated building construction safety risk management[J].Construction Safety,2017(5):13-15.
[11]陈伟,周曼,叶家军,等.建筑工程安全施工费费率测算的PSO-BP模型研究[J].中国安全科学学报,2016,26(5):146-151.CHEN Wei,ZHOU Man,YE Jiajun,et al.PSO-BP modeling research on fee rate measurement of construction project safe construction cost[J].China Safety Science Journal,2016,26(5):146-151.
[12]李秀云,杨高升,李杰.最优安全文明施工措施费费率计取模型的构建与应用[J].中国安全生产科学技术,2013,9(9):147-151.LI Xiuyun,YANG Gaosheng,LI Jie.Establishment and application of accrual model about optimal safe and civilized construction measures fee rates[J].Journal of Safety Science and Technology,2013,9(9):147-151.
[13]GB50854—2013,房屋建筑与装饰工程计量规范[S].GB50854-2013,Standard method of measurement for building construction and fitting-out works[S].
[14]周景阳,张明君.工业化住宅措施项目费实证及计价对策研究[J].建筑经济,2016,37(11):56-60.ZHOU Jingyang,ZHANG Mingjun.Empirical research on the measurement item cost of industrialized housing and pricing strategy[J].Construction Economy,2016,37(11):56-60.
[15]历光大.装配式混凝土结构在住宅产业化中的应用研究[D].北京:北京建筑大学,2016.LI Guangda.Study on the application of assembled concrete structure in housing industry[D].Beijing:Beijing University of Civil Engineering and Architechture,2016.
[16]湖北省住房与城乡建设厅.湖北省装配式建筑装配率计算规则(试行)[L].2017-07-12.
[17]PAWLAK Z.Roset sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[18]胡祝华,夏勇,彭金莲.基于关系数据库的粗糙集约简改进算法[J].微计算机应用,2011,32(10):37-42.HU Zhuhua,XIA Yong,PENG Jinlian.An improved algorithm of rough set reduction based on relational database[J].Microcomputer Applications,2011,32(10):37-42.
[19]温廷新,陈晓宇,杨红玉,等.基于优化Bagging-LSSVM模型的冲击地压预测[J].中国安全科学学报,2017,27(6):121-126.WEN Tingxin,CHEN Xiaoyu,YANG Hongyu,et al.Research on prediction of rock burst based on optimized BaggingLSSVM model[J].Journal of Safety Science and Technology,2017,27(6):121-126.
[20]张晓晖,朱家元,张恒喜.基于LS-SVM的小样本费用智能预测[J].计算机工程与应用,2004,40(27):203-204,214.ZHANG Xiaohui,ZHU Jiayuan,ZHANG Hengxi.Cost intelligent prediction with few observations using LS-SVM[J].Computer Engineering and Applications,2004,40(27):203-204,214.