神經(jīng)網(wǎng)絡(luò)在高耐久性混凝土骨料優(yōu)選中的應(yīng)用
發(fā)布時(shí)間：2018-09-05 18:06
【摘要】：粗骨料占混凝土體積的50%以上,來源廣泛,質(zhì)量難以控制。其質(zhì)量直接影響混凝土的耐久性,有必要對(duì)其進(jìn)行優(yōu)選。以往,人們研究粗骨料對(duì)混凝土耐久性的影響大多是通過試驗(yàn)進(jìn)行的,試驗(yàn)研究費(fèi)時(shí)費(fèi)力。因此,有必要引入一種先進(jìn)的方法來預(yù)測(cè)粗骨料對(duì)混凝土耐久性的影響,從而提高效率。粗骨料與混凝土耐久性之間的關(guān)系是非線性的,很難建立明確的數(shù)學(xué)表達(dá)式,本文將人工神經(jīng)網(wǎng)絡(luò)理論引入到混凝土的研究之中。本文通過選用不同技術(shù)指標(biāo)(平均粒徑、吸水率、表觀密度、強(qiáng)度)的典型骨料,研究其對(duì)混凝土抗?jié)B、抗凍、抗碳化性的影響,結(jié)果表明:當(dāng)水灰比為0.32時(shí),粒徑(5~30 mm)較大的骨料配制的混凝土的抗?jié)B性最好;當(dāng)水灰比為0.40和0.49時(shí),粒徑(5~20 mm)適中的骨料所配制的混凝土的抗?jié)B性最好。粒徑對(duì)混凝土抗碳化性的影響規(guī)律與其對(duì)抗?jié)B性的影響規(guī)律類似,對(duì)抗凍性的影響不是很明顯。對(duì)于不同品種骨料配制的混凝土的抗?jié)B性而言,均是玄武巖配制的混凝土最好。處于低水灰比(0.32)時(shí),石灰?guī)r配制的混凝土的抗?jié)B性最差;在其它水灰比(0.40和0.49)時(shí),花崗巖配制的混凝土的抗?jié)B性最差。骨料品種對(duì)混凝土抗凍、抗碳化的影響規(guī)律與其對(duì)抗?jié)B性的影響規(guī)律相同。本文基于以上混凝土耐久性試驗(yàn)所得數(shù)據(jù),建立了混凝土耐久性的神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型。在建立模型之前,本文對(duì)較為常用的四種優(yōu)化的神經(jīng)網(wǎng)絡(luò)算法(彈性梯度下降法、附加動(dòng)量法、自適應(yīng)學(xué)習(xí)率算法、L-M算法)進(jìn)行比較,發(fā)現(xiàn)L-M算法無論是在訓(xùn)練時(shí)間還是在預(yù)測(cè)誤差和收斂性方面都具有其它三種算法無法比擬的優(yōu)勢(shì),本文選擇L-M算法進(jìn)行訓(xùn)練。綜合分析粗骨料對(duì)混凝土耐久性的影響因素之后,選定水灰比、齡期、平均粒徑、吸水率、表觀密度和強(qiáng)度6個(gè)因素作為混凝土抗?jié)B性和抗碳化性預(yù)測(cè)模型的輸入變量,分別以氯離子擴(kuò)散系數(shù)、碳化深度作為輸出變量,基于108組抗?jié)B數(shù)據(jù)和135組抗碳化數(shù)據(jù),分別建立結(jié)構(gòu)為6-17-1和6-15-1的混凝土抗?jié)B性與抗碳化性預(yù)測(cè)模型。選定水灰比、凍融循環(huán)次數(shù)、平均粒徑、吸水率、表觀密度和強(qiáng)度6個(gè)因素作為混凝土抗凍性預(yù)測(cè)模型的輸入變量,以相對(duì)動(dòng)彈性模量作為輸出變量,基于抗凍試驗(yàn)所得103組數(shù)據(jù)建立結(jié)構(gòu)為6-21-1的混凝土抗凍性預(yù)測(cè)模型。運(yùn)用以上建立的混凝土抗?jié)B、抗凍、抗碳化性預(yù)測(cè)模型對(duì)測(cè)試樣本進(jìn)行預(yù)測(cè),平均預(yù)測(cè)誤差分別為4.44%、4.15%、5.16%,均在6%以內(nèi),預(yù)測(cè)值與試驗(yàn)值非常接近,可以滿足實(shí)際工程的需要。為了驗(yàn)證建立的模型的適用性,本文以抗?jié)B、抗凍性預(yù)測(cè)模型為例,收集了一些工程中相關(guān)數(shù)據(jù),利用已建立的神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型分別對(duì)其進(jìn)行預(yù)測(cè)。預(yù)測(cè)誤差分別為11.00%、9.85%,比本文對(duì)試驗(yàn)數(shù)據(jù)的預(yù)測(cè)誤差稍大一些,但是可以滿足工程要求。
[Abstract]:Coarse aggregate accounts for more than 50% of the volume of concrete. The quality of concrete directly affects the durability of concrete, it is necessary to select it. In the past, the influence of coarse aggregate on concrete durability was mostly studied through experiments, which were time-consuming and laborious. Therefore, it is necessary to introduce an advanced method to predict the effect of coarse aggregate on the durability of concrete so as to improve the efficiency. The relationship between coarse aggregate and concrete durability is nonlinear, it is difficult to establish a clear mathematical expression. In this paper, the artificial neural network theory is introduced into the study of concrete. In this paper, the effects of different technical indexes (average particle size, water absorption, apparent density and strength) on the impermeability, freezing resistance and carbonation resistance of concrete are studied. The results show that when the water-cement ratio is 0.32, When the water-cement ratio is 0.40 and 0.49, the impermeability of concrete with moderate particle size (5 ~ 20 mm) is the best. The effect of particle size on the carbonation resistance of concrete is similar to that on the impermeability of concrete, but the effect on freezing resistance is not obvious. For the impermeability of concrete prepared with different kinds of aggregate, the concrete made of basalt is the best. When the water-cement ratio is low (0.32), the impermeability of concrete prepared by limestone is the worst, and the impermeability of concrete prepared by granite is the worst when the other water-cement ratio (0.40 and 0.49). The effect of aggregate variety on the frost resistance and carbonation resistance of concrete is the same as that of its impermeability. Based on the above data of concrete durability test, a neural network prediction model of concrete durability is established in this paper. Before establishing the model, four kinds of optimization neural network algorithms (elastic gradient descent method, additional momentum method, adaptive learning rate algorithm) are compared in this paper. It is found that L-M algorithm has incomparable advantages in terms of training time, prediction error and convergence. This paper chooses L-M algorithm to train. After synthetically analyzing the influence factors of coarse aggregate on concrete durability, six factors, such as water-cement ratio, age, average particle size, water absorption, apparent density and strength, are selected as input variables of the prediction model of concrete impermeability and carbonation resistance. Taking chloride diffusion coefficient and carbonation depth as output variables, based on 108 groups of anti-seepage data and 135 groups of anti-carbonation data, the prediction models of anti-permeability and anti-carbonation of concrete with structures of 6-17-1 and 6-15-1 were established, respectively. Six factors, such as water-cement ratio, freeze-thaw cycle times, average particle size, water absorption, apparent density and strength, are selected as input variables and relative dynamic modulus of elasticity as output variables. Based on 103 sets of data obtained from frost resistance test, a concrete frost resistance prediction model with structure of 6-21-1 was established. The prediction model of concrete impermeability, frost resistance and carbonization resistance established above is used to predict the test samples. The average prediction error is 4.44 / 4.15 / 5.16, which is less than 6% respectively. The predicted value is very close to the test value, which can meet the needs of practical engineering. In order to verify the applicability of the established model, the prediction model of impermeability and frost resistance is taken as an example, and some relevant data in engineering are collected, and the established neural network prediction model is used to predict the model respectively. The prediction error is 11.00 and 9.85, which is a little larger than that of the test data in this paper, but it can meet the engineering requirements.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TU528.041
本文編號(hào)：2225045
[Abstract]:Coarse aggregate accounts for more than 50% of the volume of concrete. The quality of concrete directly affects the durability of concrete, it is necessary to select it. In the past, the influence of coarse aggregate on concrete durability was mostly studied through experiments, which were time-consuming and laborious. Therefore, it is necessary to introduce an advanced method to predict the effect of coarse aggregate on the durability of concrete so as to improve the efficiency. The relationship between coarse aggregate and concrete durability is nonlinear, it is difficult to establish a clear mathematical expression. In this paper, the artificial neural network theory is introduced into the study of concrete. In this paper, the effects of different technical indexes (average particle size, water absorption, apparent density and strength) on the impermeability, freezing resistance and carbonation resistance of concrete are studied. The results show that when the water-cement ratio is 0.32, When the water-cement ratio is 0.40 and 0.49, the impermeability of concrete with moderate particle size (5 ~ 20 mm) is the best. The effect of particle size on the carbonation resistance of concrete is similar to that on the impermeability of concrete, but the effect on freezing resistance is not obvious. For the impermeability of concrete prepared with different kinds of aggregate, the concrete made of basalt is the best. When the water-cement ratio is low (0.32), the impermeability of concrete prepared by limestone is the worst, and the impermeability of concrete prepared by granite is the worst when the other water-cement ratio (0.40 and 0.49). The effect of aggregate variety on the frost resistance and carbonation resistance of concrete is the same as that of its impermeability. Based on the above data of concrete durability test, a neural network prediction model of concrete durability is established in this paper. Before establishing the model, four kinds of optimization neural network algorithms (elastic gradient descent method, additional momentum method, adaptive learning rate algorithm) are compared in this paper. It is found that L-M algorithm has incomparable advantages in terms of training time, prediction error and convergence. This paper chooses L-M algorithm to train. After synthetically analyzing the influence factors of coarse aggregate on concrete durability, six factors, such as water-cement ratio, age, average particle size, water absorption, apparent density and strength, are selected as input variables of the prediction model of concrete impermeability and carbonation resistance. Taking chloride diffusion coefficient and carbonation depth as output variables, based on 108 groups of anti-seepage data and 135 groups of anti-carbonation data, the prediction models of anti-permeability and anti-carbonation of concrete with structures of 6-17-1 and 6-15-1 were established, respectively. Six factors, such as water-cement ratio, freeze-thaw cycle times, average particle size, water absorption, apparent density and strength, are selected as input variables and relative dynamic modulus of elasticity as output variables. Based on 103 sets of data obtained from frost resistance test, a concrete frost resistance prediction model with structure of 6-21-1 was established. The prediction model of concrete impermeability, frost resistance and carbonization resistance established above is used to predict the test samples. The average prediction error is 4.44 / 4.15 / 5.16, which is less than 6% respectively. The predicted value is very close to the test value, which can meet the needs of practical engineering. In order to verify the applicability of the established model, the prediction model of impermeability and frost resistance is taken as an example, and some relevant data in engineering are collected, and the established neural network prediction model is used to predict the model respectively. The prediction error is 11.00 and 9.85, which is a little larger than that of the test data in this paper, but it can meet the engineering requirements.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TU528.041
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 萬廣培;李化建;黃佳木;;混凝土內(nèi)養(yǎng)護(hù)技術(shù)研究進(jìn)展[J];混凝土;2012年07期
2 王愛勤,張承志;水工混凝土的堿骨料反應(yīng)問題[J];水利學(xué)報(bào);2003年02期
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