【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(guò)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(guò)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(guò)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, pavement material performance research has always been a hot topic in the direction of road engineering. In this paper, the dynamic modulus of asphalt mixture, dynamic modulus of recycled asphalt mixture, autogenous shrinkage of concrete and other pavement material performance prediction models are studied by genetic expression programming algorithm. The analysis results have important practical significance for road engineering, structural engineering and other fields. Firstly, this paper systematically analyzes the research status of dynamic modulus of asphalt mixture, dynamic modulus of recycled asphalt mixture and autogenous shrinkage of concrete. The main research contents can be summarized as follows: (1) The genetic expression programming algorithm is used to predict the dynamic modulus of asphalt mixture, and the eight main factors affecting the dynamic modulus of asphalt mixture are: the void fraction (aV) of asphalt mixture. Available bitumen content (b effV), bitumen viscosity (_), loading frequency (f), cumulative residual mass fraction (34?, 38?, 4?) of bitumen mixture after 19.0, 9.5, 4.75 mm sieve, and the throughput of bitumen mixture on 0.075 mm sieve hole (200?) constitute the main parameters of predicting the dynamic modulus model of bitumen mixture. Through eight discrete parameters A prediction model of dynamic modulus of asphalt mixture based on genetic expression programming algorithm is established.The results show that the predicted values of dynamic modulus obtained by the prediction model are highly correlated with the measured values.The prediction model is compared with Witczak 1999 function model,Korea dynamic modulus prediction model and 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) Using the genetic expression programming algorithm to study the dynamic modulus prediction model of recycled asphalt mixture. Eight influencing factors in the prediction model are added to the mixture ratio of recycled asphalt pavement mixture as nine input parameters of the dynamic modulus of recycled asphalt mixture. The results show that the predicted value of the predicted model has a good fit with the measured value, and it has a certain reference value for the study of the dynamic modulus of recycled asphalt mixture. (3) Concrete. The autogenous shrinkage test was carried out to analyze the effect of different water cement ratio and silica fume content on the autogenous shrinkage of concrete.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 and curing age) was studied by genetic expression programming. The results show that the prediction model of concrete autogenous shrinkage based on genetic expression programming meets the requirements of engineering design in precision and has certain guidance for concrete structure design. Finally, on the basis of a comprehensive summary of the full-text work, 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(lèi)號(hào)】：U414

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本文編號(hào)：2187654