基于级联神经网络的建筑业人工成本预测研究

发布时间：2018-04-25 20:21

本文选题：建筑成本管理 + 人工成本　；参考：《重庆大学》2013年硕士论文

【摘要】：建筑业是我国的传统行业，也是支柱产业，随着国际建筑市场的逐渐打开，先进的技术施工工艺以及建设管理方式给我国建筑业的发展注入了新鲜血液。在“十二五”期间全国建筑业总产值、增加值年均计划增长15%以上。而如今，劳动密集型的建筑业面对人工成本的逐年上涨，民工荒的大面积出现以及新生代农民工从业意愿降低等问题，建筑业人工成本的管理将面临巨大挑战，而准确预测人工成本则是良好管控人工成本的基础。本研究回顾了人工成本概念与构成，分析了目前国内外建筑业人工成本管理存在的系列现实问题，以期研究分析建筑业人工成本的影响因素，建立较准确的人工成本预测模型，从而提出优化建筑业人工成本管理的相关建议。经过大量的文献综述，本研究共识别出47个影响指标，通过与10位专业人士的结构化访谈，精简出38个相关性较大的指标，通过问卷调查，对38个影响指标评价打分，通过数据收据与归纳，运用数理统计方法进行数据分析，揭示影响我国建筑业人工成本上涨的主要因素，主要包括：宏观经济市场因素（F3）、行业因素（F4）、企业因素（F5）、建造成本因素（F2）以及施工建造因素（F1）。基于因子分析所得结论，建立人工成本预测模型，创建级联神经网络预测方法，并以重庆市为例，，对预测模型进行实证检验，检验结果证明级联神经网络模型的预测效果明显精确于传统的预测方法。通过以上研究，一方面可以完善建筑企业对人工成本管理的全面认识，提高建筑业人工成本预测的准确度。另一方面，可以促进建筑工程的利润管理和企业的经济效益，为整个建筑行业以及国家的综合发展提供基础研究依据。
[Abstract]:The construction industry is the traditional industry and the pillar industry of our country. With the opening of the international construction market, the advanced construction technology and construction management have injected fresh blood into the development of our country's construction industry. During the period of the 12th Five-Year Plan, the total output value of the national construction industry is expected to grow by more than 15 percent annually. Nowadays, the labor-intensive construction industry is faced with the problems of rising labor cost year by year, the large area of the labor shortage and the decrease of the new generation of migrant workers' willingness to work, so the management of the labor cost of the construction industry will face a huge challenge. And accurate prediction of labor cost is the basis of good control of labor cost. This study reviews the concept and composition of labor cost, analyzes the series of practical problems existing in the management of labor cost of construction industry at home and abroad, in order to study and analyze the influencing factors of labor cost of construction industry, and establish a more accurate forecasting model of labor cost. Therefore, the paper puts forward some suggestions on optimizing labor cost management in construction industry. After a large amount of literature review, 47 impact indicators were identified in this study. Through structured interviews with 10 professionals, 38 indicators with greater relevance were simplified, and 38 impact indicators were evaluated by questionnaires. By means of data receipt and induction, the main factors influencing the increase of labor cost of construction industry in China are revealed by means of mathematical statistics. The main factors include: macroeconomic market factor F3, industry factor F4, enterprise factor F5, construction cost factor F2) and construction factor F1. Based on the conclusion of factor analysis, the artificial cost forecasting model is established, and the cascaded neural network forecasting method is established, and the prediction model is tested empirically by taking Chongqing as an example. The test results show that the prediction effect of the cascaded neural network model is more accurate than that of the traditional prediction method. Through the above research, on the one hand, the comprehensive understanding of labor cost management in construction enterprises can be improved, and the accuracy of labor cost prediction in construction industry can be improved. On the other hand, it can promote the profit management of the construction project and the economic benefit of the enterprise, and provide the basic research basis for the comprehensive development of the whole construction industry and the country.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F275.3;F426.92

【参考文献】