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|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|
|Issue Date: ||2016-10-13 15:23:35 (UTC+8)|
;Identifying and developing impact factors and human allocation model for sustainable road construction
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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