本文關(guān)鍵詞： 工程項(xiàng)目質(zhì)量 風(fēng)險(xiǎn)評(píng)價(jià) 粗糙集 神經(jīng)網(wǎng)絡(luò) 指標(biāo)體系　出處：《江西理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái)，建筑業(yè)在我國(guó)得到了快速發(fā)展，建筑市場(chǎng)的規(guī)模也在逐漸地?cái)U(kuò)大，但由于工程項(xiàng)目自身具有規(guī)模大、周期長(zhǎng)、投資高等特性，使得工程項(xiàng)目的質(zhì)量問(wèn)題越來(lái)越受到大家的重視。一旦工程項(xiàng)目的質(zhì)量出現(xiàn)問(wèn)題，不僅會(huì)影響到整個(gè)工程項(xiàng)目的交付和使用，造成經(jīng)濟(jì)利益的損失，如果嚴(yán)重的話還會(huì)影響到國(guó)計(jì)民生甚至整個(gè)建筑業(yè)的發(fā)展。目前，工程項(xiàng)目的質(zhì)量問(wèn)題已經(jīng)成為人們關(guān)注的焦點(diǎn)，人們希望通過(guò)技術(shù)控制和科學(xué)管理使得工程項(xiàng)目質(zhì)量達(dá)到預(yù)期目標(biāo)，而工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)評(píng)價(jià)是工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)管理中最有效的管理方法，是發(fā)現(xiàn)質(zhì)量問(wèn)題、找出問(wèn)題原因和保證工程項(xiàng)目質(zhì)量的重要依據(jù)。因此，本文對(duì)工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)評(píng)價(jià)展開(kāi)了理論和實(shí)證研究，希望能夠給我國(guó)的工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)管理提供一定的理論依據(jù)。 本文首先從工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)管理入手，進(jìn)而對(duì)工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)管理的相關(guān)概念和特點(diǎn)展開(kāi)了詳細(xì)的論述。然后分析了工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)管理的全過(guò)程，逐一對(duì)傳統(tǒng)的風(fēng)險(xiǎn)識(shí)別和風(fēng)險(xiǎn)評(píng)價(jià)方法進(jìn)行了相關(guān)介紹，并結(jié)合工程項(xiàng)目自身的特點(diǎn)，提出了基于粗糙集_神經(jīng)網(wǎng)絡(luò)的工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)評(píng)價(jià)方法，同時(shí)論證了該評(píng)價(jià)方法的可行性。接下來(lái)根據(jù)工程項(xiàng)目施工階段的實(shí)際情況，運(yùn)用相關(guān)理論和方法識(shí)別出工程項(xiàng)目施工階段的質(zhì)量風(fēng)險(xiǎn)因素，構(gòu)建出工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)評(píng)價(jià)指標(biāo)體系。最后對(duì)粗糙集_神經(jīng)網(wǎng)絡(luò)風(fēng)險(xiǎn)評(píng)價(jià)模型進(jìn)行了實(shí)證性分析。首先利用粗糙集理論對(duì)收集到的工地樣本數(shù)據(jù)進(jìn)行處理，然后將約簡(jiǎn)后的評(píng)價(jià)指標(biāo)作為輸入端建立了工程項(xiàng)目質(zhì)量風(fēng)險(xiǎn)的神經(jīng)網(wǎng)絡(luò)評(píng)價(jià)模型，，并利用MATLAB7.0軟件工具對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練和檢驗(yàn)。通過(guò)實(shí)證分析的結(jié)果可以看出該模型的可操作性較好，評(píng)價(jià)的效果不錯(cuò)，具有一定的應(yīng)用價(jià)值。
[Abstract]:In recent years, the construction industry has been developing rapidly in our country, and the scale of the construction market is also gradually expanding. However, because of the large scale, long period, high investment and so on, the engineering project itself has the characteristics of large scale, long period, high investment and so on. The quality problem of the engineering project is paid more and more attention to. Once the quality of the project has a problem, it will not only affect the delivery and use of the whole project, but also cause the loss of economic benefits. If it is serious, it will also affect the development of the national economy and the people's livelihood and even the entire construction industry. At present, the quality of engineering projects has become the focus of attention. It is hoped that the project quality can reach the expected goal through technical control and scientific management, and the engineering project quality risk assessment is the most effective management method in the engineering project quality risk management, which is to find the quality problem. To find out the cause of the problem and the important basis to guarantee the quality of engineering project, this paper has carried out theoretical and empirical research on quality risk assessment of engineering project, hoping to provide some theoretical basis for quality risk management of engineering project in our country. This paper begins with the quality risk management of engineering projects, and then discusses in detail the related concepts and characteristics of quality risk management of engineering projects, and then analyzes the whole process of quality risk management of engineering projects. This paper introduces the traditional risk identification and risk assessment methods one by one, and puts forward the quality risk evaluation method based on rough set _ neural network, combining with the characteristics of engineering project itself. At the same time, the feasibility of the evaluation method is demonstrated. Then, according to the actual situation of the construction phase of the project, the quality risk factors of the construction phase of the project are identified by using the relevant theories and methods. The quality risk evaluation index system of engineering project is constructed. Finally, the rough set _ neural network risk evaluation model is analyzed empirically. Firstly, the collected site sample data are processed by rough set theory. Then the reduced evaluation index is used as the input to establish the neural network evaluation model of engineering project quality risk. The MATLAB7.0 software is used to train and test BP neural network. The results of empirical analysis show that the model has good operability, good evaluation effect and certain application value.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TU712.3

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