【摘要】：城市道路建設(shè)是市政工程的重要組成部分,對日常生活和經(jīng)濟(jì)發(fā)展具有深遠(yuǎn)影響。近年來,國家對城市道路建設(shè)的重視程度不斷提高,對城市道路建設(shè)的投資也越來越多。然而,隨著大批城市道路工程雨后春筍般的涌現(xiàn),相應(yīng)的工程管理卻沒有跟上時(shí)代的步伐,造價(jià)失控的現(xiàn)象愈發(fā)嚴(yán)重。特別是那些大型的道路工程,本身結(jié)構(gòu)復(fù)雜,不易估算工期,加之受多種因素影響,造價(jià)計(jì)算十分繁瑣,具有很大的模糊性和不確定性,難免在投資過程中出現(xiàn)資金浪費(fèi)或者資金不足的現(xiàn)象,給國家和單位造成了巨大的損失,給經(jīng)濟(jì)發(fā)展也帶來了不利的影響。要控制道路工程的投資風(fēng)險(xiǎn),就必須準(zhǔn)確的估算造價(jià)。針對這一問題,很多國內(nèi)外學(xué)者提出了相應(yīng)的解決方案,建立了多種數(shù)學(xué)模型,如BCIS法、蒙特卡羅隨機(jī)模擬估算模型等。然而,這些方法大都憑借工程經(jīng)驗(yàn)來估算道路工程成本,具有一定的主觀性,難以準(zhǔn)確計(jì)算道路工程造價(jià)。為解決上述問題,本文提出基于擴(kuò)展BP網(wǎng)絡(luò)的城市道路造價(jià)預(yù)測方法。神經(jīng)網(wǎng)絡(luò)在道路工程造價(jià)的預(yù)測中有三個(gè)問題難以解決,一是影響造價(jià)的因素難以確定,二是傳統(tǒng)BP網(wǎng)絡(luò)的精度和穩(wěn)定性有待提高,三是訓(xùn)練樣本和檢驗(yàn)樣本的質(zhì)量難以保證。針對第一個(gè)問題,本文利用每種影響因素的貢獻(xiàn)度來決定該因素的保留與否,即利用BP網(wǎng)絡(luò)計(jì)算每種因素對造價(jià)誤差的影響,然后確定該因素對造價(jià)計(jì)算是否具有貢獻(xiàn)。針對第二個(gè)問題,本文擬采用擴(kuò)展BP網(wǎng)絡(luò),用新的網(wǎng)絡(luò)模型和混合訓(xùn)練算法代替?zhèn)鹘y(tǒng)的算法,提高道路工程造價(jià)的準(zhǔn)確性和穩(wěn)定性。針對第三個(gè)問題,本文采用歐氏距離法和k-means算法消除冗余數(shù)據(jù)和誤差較大的數(shù)據(jù)。本文最后設(shè)計(jì)了一組實(shí)驗(yàn),收集了2012—2014年濟(jì)南市部分城區(qū)市政道路工程的部分信息,將這些信息分成訓(xùn)練樣本和檢驗(yàn)樣本對本文算法進(jìn)行驗(yàn)證。最終的實(shí)驗(yàn)結(jié)果表明,本文的方法是切實(shí)可行的,進(jìn)一步提高了道路工程造價(jià)預(yù)測的準(zhǔn)確性,對成本控制、降低工程風(fēng)險(xiǎn)具有重要的現(xiàn)實(shí)意義。
[Abstract]:Urban road construction is an important part of municipal engineering, which has a profound impact on daily life and economic development. In recent years, more and more attention has been paid to urban road construction and more investment has been made in urban road construction. However, with a large number of urban road projects springing up, the corresponding engineering management has not kept up with the pace of the times, and the phenomenon of out-of-control cost is becoming more and more serious. Especially those large-scale road projects, which are complicated in structure and difficult to estimate the duration of the project, together with the influence of many factors, the cost calculation is very complicated, with great fuzziness and uncertainty. It is inevitable that the phenomenon of capital waste or lack of funds in the process of investment has caused great losses to the country and the unit, and has also brought adverse effects to the economic development. In order to control the investment risk of road engineering, it is necessary to estimate the cost accurately. In order to solve this problem, many scholars at home and abroad have put forward corresponding solutions and established various mathematical models, such as BCIS method, Monte Carlo stochastic simulation model and so on. However, most of these methods rely on engineering experience to estimate the cost of road engineering, which is subjective and difficult to accurately calculate the cost of road engineering. In order to solve the above problems, this paper presents a method of urban road cost prediction based on extended BP network. It is difficult to solve three problems in the prediction of road engineering cost by neural network. One is that the factors influencing the cost are difficult to determine; the other is the accuracy and stability of the traditional BP network need to be improved; the third is the quality of the training samples and the test samples is difficult to guarantee. In order to solve the first problem, the contribution degree of each factor is used to determine whether the factor is retained or not, that is, the influence of each factor on the cost error is calculated by using BP network, and then the contribution of the factor to the cost calculation is determined. In order to improve the accuracy and stability of road engineering cost, this paper proposes to use extended BP network to replace the traditional algorithm with new network model and hybrid training algorithm. To solve the third problem, Euclidean distance method and k-means algorithm are used to eliminate redundant data and large error data. Finally, a set of experiments are designed to collect some information of municipal road engineering in Jinan from 2012 to 2014. The information is divided into training samples and test samples to verify the algorithm. The final experimental results show that the proposed method is feasible, and further improves the accuracy of road engineering cost prediction, and has important practical significance for cost control and reduction of engineering risk.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U415.13

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本文編號(hào)：2253009