【摘要】：作為路面結(jié)構(gòu)設(shè)計(jì)和施工的重要依據(jù),路面材料性能的研究一直是道路工程方向研究的熱點(diǎn)。本文通過基因表達(dá)式編程算法對(duì)瀝青混合料動(dòng)態(tài)模量、再生瀝青混合料動(dòng)態(tài)模量、混凝土自收縮等路面材料性能預(yù)測(cè)模型進(jìn)行研究,所得到的分析成果對(duì)道路工程、結(jié)構(gòu)工程等領(lǐng)域具有重要的實(shí)際意義。本文首先系統(tǒng)分析了瀝青混合料動(dòng)模量、再生瀝青混合料動(dòng)模量、混凝土自收縮的研究現(xiàn)狀。然后,借鑒前人的研究成果,應(yīng)用基因表達(dá)式編程算法基本理論,對(duì)瀝青混合料動(dòng)模量、再生瀝青混合料動(dòng)模量、混凝土自收縮進(jìn)行了比較深入的分析研究。主要研究?jī)?nèi)容可總結(jié)如下幾點(diǎn):(1)采用基因表達(dá)式編程算法進(jìn)行瀝青混合料動(dòng)態(tài)模量的預(yù)測(cè),以瀝青混合料動(dòng)態(tài)模量的八個(gè)主要影響因素:瀝青混合料的空隙率(aV),有效瀝青含量(b effV),瀝青黏度(η),荷載頻率(f),瀝青混合料在19.0、9.5、4.75mm號(hào)篩篩后的累計(jì)篩余質(zhì)量分?jǐn)?shù)(34?、38?、4?),及瀝青混合料在0.075mm號(hào)篩篩孔上的通過率(200?),構(gòu)成預(yù)測(cè)瀝青混合料動(dòng)態(tài)模量模型的主要參數(shù),通過對(duì)八個(gè)離散的參數(shù)建立基于基因表達(dá)式編程算法的瀝青混合料動(dòng)態(tài)模量預(yù)測(cè)模型。結(jié)果表明:預(yù)測(cè)模型得到的動(dòng)態(tài)模量預(yù)測(cè)值與實(shí)測(cè)值之間具有較高的相關(guān)性;并將預(yù)測(cè)模型與Witczak1999年函數(shù)模型,韓國(guó)動(dòng)態(tài)模量預(yù)測(cè)模型以及人工神經(jīng)網(wǎng)絡(luò)模型進(jìn)行比較分析,結(jié)果表明基因表達(dá)式編程算法預(yù)測(cè)瀝青混合料動(dòng)態(tài)模量具有簡(jiǎn)單可靠的優(yōu)點(diǎn)。(2)應(yīng)用基因表達(dá)式編程算法研究再生瀝青混合料動(dòng)態(tài)模量預(yù)測(cè)模型,基于熱拌瀝青混合料動(dòng)模量的研究成果,參照采用Witczak 1999瀝青混合料動(dòng)模量預(yù)測(cè)模型中8個(gè)影響因素,另外增加回收瀝青路面混合料的摻配比,作為再生瀝青混合料動(dòng)模量的9個(gè)輸入?yún)?shù)。采用基因表達(dá)式編程算法對(duì)9個(gè)影響因素離散分析得到再生瀝青混合料動(dòng)態(tài)模量預(yù)測(cè)模型。分析動(dòng)模量預(yù)測(cè)值與實(shí)測(cè)值的擬合度以及動(dòng)態(tài)模量與摻配比之間的相關(guān)性。并對(duì)各影響因素與動(dòng)模量之間的敏感性進(jìn)行了分析。結(jié)果表明:預(yù)測(cè)模型得到的預(yù)測(cè)值與實(shí)測(cè)值之間具有較好的擬合度,對(duì)再生瀝青混合料動(dòng)態(tài)模量的研究具有一定的參考價(jià)值。(3)進(jìn)行混凝土自收縮試驗(yàn),分析不同水灰比、硅粉摻加量對(duì)自收縮的影響。運(yùn)用基因表達(dá)式編程算法研究混凝土自收縮與其主要影響因素(水灰比、礦物摻合料、骨料含量、水泥漿體含量、高效減水劑、養(yǎng)護(hù)溫度、養(yǎng)護(hù)齡期)之間的關(guān)系,得到混凝土自收縮預(yù)測(cè)模型,并將自收縮預(yù)測(cè)值與實(shí)測(cè)值進(jìn)行擬合度分析,在此基礎(chǔ)上,分析各主要影響因素與自收縮之間的相關(guān)性。研究表明:基因表達(dá)式編程算法的混凝土自收縮預(yù)測(cè)模型在精度上符合工程設(shè)計(jì)要求,對(duì)混凝土結(jié)構(gòu)設(shè)計(jì)有一定的指導(dǎo)意義。最后,在全面總結(jié)全文工作的基礎(chǔ)上,對(duì)進(jìn)一步的研究工作提出了一些建議與展望。
[Abstract]:As an important basis for pavement structure design and construction, the research of pavement material performance has been a hot spot in road engineering. In this paper, the prediction model of pavement material performance, such as dynamic modulus of asphalt mixture, dynamic modulus of recycled asphalt mixture, self-shrinkage of concrete and so on, is studied by genetic expression programming algorithm. Structural engineering and other fields have important practical significance. In this paper, the dynamic modulus of asphalt mixture, the dynamic modulus of recycled asphalt mixture and the self-shrinkage of concrete are systematically analyzed. Then, the dynamic modulus of asphalt mixture, the dynamic modulus of recycled asphalt mixture and the autogenous shrinkage of concrete are analyzed and studied by using the basic theory of genetic expression programming algorithm for reference. The main research contents can be summarized as follows: (1) using genetic expression programming algorithm to predict the dynamic modulus of asphalt mixture. Eight main influencing factors of dynamic modulus of asphalt mixture are as follows: void rate of asphalt mixture (aV), effective asphalt content (b effV), asphalt viscosity (畏), cumulative residual mass fraction of (f), asphalt mixture after screening with load frequency of 19.0 ~ 9.5 ~ 4.75 mm (34 ~ 38 ~ (4?), and The passing rate of asphalt mixture on the 0.075mm sieve (200?) constitutes the main parameter of predicting the dynamic modulus model of asphalt mixture. The prediction model of asphalt mixture dynamic modulus based on genetic expression programming algorithm is established for eight discrete parameters. The results show that there is a high correlation between the predicted values and the measured values, and the prediction model is compared with the Witczak1999 annual function model, the Korean dynamic modulus prediction model and the artificial neural network model. The results show that the genetic expression programming algorithm is simple and reliable in predicting the dynamic modulus of asphalt mixture. (2) the prediction model of dynamic modulus of recycled asphalt mixture is studied by genetic expression programming algorithm. Based on the research results of dynamic modulus of hot mix asphalt mixture, eight factors of dynamic modulus prediction model of Witczak 1999 asphalt mixture are adopted, and the ratio of recycled asphalt pavement mixture is increased. As nine input parameters of dynamic modulus of recycled asphalt mixture. The prediction model of dynamic modulus of recycled asphalt mixture was obtained by genetic expression programming algorithm. The relationship between the dynamic modulus prediction value and the measured value and the correlation between the dynamic modulus and the mixing ratio is analyzed. The sensitivity between the influencing factors and the dynamic modulus is analyzed. The results show that there is a good fit between the predicted value and the measured value, and it has certain reference value for the study of dynamic modulus of recycled asphalt mixture. (3) Auto-shrinkage test of concrete is carried out, and different water-cement ratio is analyzed. The effect of adding amount of silica fume on autogenous shrinkage. The relationship between autogenous shrinkage of concrete and its main influencing factors (water-cement ratio, mineral admixture, aggregate content, cement paste content, superplasticizer, curing temperature, curing age) was studied by genetic expression programming algorithm. The prediction model of autogenous shrinkage of concrete is obtained, and the fitting degree between the predicted value of autogenous shrinkage and the measured value is analyzed. On the basis of this, the correlation between the main influencing factors and autogenous shrinkage is analyzed. The results show that the prediction model of self-shrinkage of concrete based on genetic expression programming algorithm accords with the requirement of engineering design in precision, and has certain guiding significance for the design of concrete structure. Finally, on the basis of summing up the whole paper, some suggestions and prospects for further research are put forward.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U414

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本文編號(hào)：2187655