本文選題：建筑成本管理 + 人工成本��； 參考：《重慶大學》2013年碩士論文

【摘要】：建筑業(yè)是我國的傳統(tǒng)行業(yè)，也是支柱產(chǎn)業(yè)，隨著國際建筑市場的逐漸打開，先進的技術施工工藝以及建設管理方式給我國建筑業(yè)的發(fā)展注入了新鮮血液。在“十二五”期間全國建筑業(yè)總產(chǎn)值、增加值年均計劃增長15%以上。而如今，勞動密集型的建筑業(yè)面對人工成本的逐年上漲，民工荒的大面積出現(xiàn)以及新生代農(nóng)民工從業(yè)意愿降低等問題，建筑業(yè)人工成本的管理將面臨巨大挑戰(zhàn)，而準確預測人工成本則是良好管控人工成本的基礎。 本研究回顧了人工成本概念與構成，分析了目前國內(nèi)外建筑業(yè)人工成本管理存在的系列現(xiàn)實問題，以期研究分析建筑業(yè)人工成本的影響因素，建立較準確的人工成本預測模型，從而提出優(yōu)化建筑業(yè)人工成本管理的相關建議。經(jīng)過大量的文獻綜述，本研究共識別出47個影響指標，通過與10位專業(yè)人士的結構化訪談，精簡出38個相關性較大的指標，通過問卷調(diào)查，對38個影響指標評價打分，通過數(shù)據(jù)收據(jù)與歸納，運用數(shù)理統(tǒng)計方法進行數(shù)據(jù)分析，揭示影響我國建筑業(yè)人工成本上漲的主要因素，主要包括：宏觀經(jīng)濟市場因素（F3）、行業(yè)因素（F4）、企業(yè)因素（F5）、建造成本因素（F2）以及施工建造因素（F1）。基于因子分析所得結論，建立人工成本預測模型，創(chuàng)建級聯(lián)神經(jīng)網(wǎng)絡預測方法，并以重慶市為例，，對預測模型進行實證檢驗，檢驗結果證明級聯(lián)神經(jīng)網(wǎng)絡模型的預測效果明顯精確于傳統(tǒng)的預測方法。 通過以上研究，一方面可以完善建筑企業(yè)對人工成本管理的全面認識，提高建筑業(yè)人工成本預測的準確度。另一方面，可以促進建筑工程的利潤管理和企業(yè)的經(jīng)濟效益，為整個建筑行業(yè)以及國家的綜合發(fā)展提供基礎研究依據(jù)。
[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.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F275.3;F426.92

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