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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72485


    Title: 永續道路工程影響因子與人力推估模式之建立
    Authors: 林尚儀;Lin,Shang-I
    Contributors: 營建管理研究所
    Keywords: 永續性道路影響因子;因素分析;專案工程人力;約略集;類神經網路;sustainable road construction;factor analysis;human allocation;rough set;artificial neural network
    Date: 2016-07-18
    Issue Date: 2016-10-13 15:23:35 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 永續道路工程影響因子與人力推估模式
    摘要
    公共建設是推動國家總體經濟發展常用作法,道路工程為主要項目之一,隨著全球氣候變遷加劇,世界各國開始意識到永續發展之重要性。從道路工程全生命週期的角度來看,包含規劃、設計、施工及後續的維護管理等各階段,又因為道路工程往往須投注相當可觀之經費與社會成本,所以在規劃設計階段,即應從「永續」觀點計算投注的總體成本到未來的預期效益,進行完整的成本效益評估。而在進入施工階段後,則因為人力短缺情形日益嚴重,產生工程落後、影響完工期程等現象,並衍生履約爭議問題。
    本研究第1階段利用問卷調查方式,發放了120份問卷並針對所回收之54樣本,採用敘述性統計分析刪除無效因子,再以因素分析萃取出之潛伏因素,分析後獲得成本方面有3個潛伏因素,包括9個因子,效益方面有2個潛伏因素,包括6個因子。其目的在確立永續性道路工程影響因素及量化影響程度與權重,供道路工程從業人員在規劃設計階段中納入考量,確保永續概念的有效落實。第2階段則利用類神經網路及約略集加上類神經網路的方法,建立評估專案工程人力的2種評估模式,其中約略集加上類神經網路的評估模式,具有88.63%的平均準確率,足已有效證明其可行性,期藉由本研究所建立的模型,應可以在專案工程人力分配預估上,成為一個有用的工具,並可對專案工程主辦機關提出預警機制,有效避免人力短缺現象產生。

    關鍵詞:永續性道路影響因子、因素分析、專案工程人力、約略集、類神經網路。
    ;Identifying and developing impact factors and human allocation model for sustainable road construction

    ABSTRACT

    This research is firstly to identify impact factors in both cost and benefit aspects using quantitative techniques and then to determine their corresponding weights for sustainable road engineering projects. The second objective is to develop a human allocation model for sustainable road construction based on the findings from the first goal. The impact factors are initially gathered from literature review and expert interviews, resulting in a total of 10 factors for questionnaire development. A 5-scale Likert questionnaire is accordingly developed for a survey. With the fulfillment of statistical criteria, 54 of 120 questionnaires are returned and a reliability test is employed to examine sampling adequacy in the beginning stage of data analysis. Therefore, we are able to identify the impact factors by the use of eight tests of missing value, mean, standard deviation, skewness, t-testing, correlation coefficients, factor loading, and measures of sampling adequacy (MSA). To determine the weight of each factor, the principle component analysis combined with orthogonal rotation best fit this research. Therefore, the analysis yields the results showing that 3 components include 9 factors in the cost aspect and 2 components include 6 factors in the benefit aspect. The finding is anticipated to benefit practitioners in the designing, planning, budgeting, and controlling phases of road engineering projects.

    Based on the finding, a database for assessing human resource allocation in pavement engineering was established by collecting detailed information from various construction projects. Fourteen influence factors were summarized through literature review and consultation with experts in the field. Thirty two road-smoothing projects were then randomly selected. Using the rough set approach and an artificial neural network model, a model for assessing human resource allocation in pavement engineering was developed. The model validity is verified by an average accuracy of 88.63%. Therefore, this proposed model can be viewed as a useful tool for estimating human resource demand in pavement engineering. It can also effectively alert the authority to avoid a shortage in manpower, preventing the construction project from falling behind schedule or even early termination as a result of inappropriate resource allocation.

    Keywords: sustainable road construction, human allocation, questionnaire survey, factor analysis, rough set, pavement engineering, artificial neural network.
    Appears in Collections:[營建管理研究所 ] 博碩士論文

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