水泥加固红土的力学特性及神经网络模型研究
|
摘要
本文在综合分析国内外关于土体加固及加固机理、微结构、神经网络模型应用等研究现状的基础上,针对存在的问题并结合云南广泛应用红土的力学特性不能完全满足工程要求的情况,提出了“水泥加固红土的力学特性及神经网络模型研究”的问题。针对云南典型红土,选用水泥作为强固剂,通过对水泥加固红土宏微观试验的对比研究,明确了水泥加固红土的力学特性及微结构特征；运用图像处理技术,提取分析了水泥加固红土的微结构特征参数；结合水泥加固红土的力学特性、微结构特征及特征参数,从微结构的角度阐明了水泥加固红土的加固机理；最后运用神经网络理论,建立起水泥加固红土抗剪强度的神经网络模型。
水泥加固红土的击实、抗剪强度、压缩、渗透等各个力学特性是在考虑水泥加入比例和试样养护时间的基础上,通过击实、直剪、压缩、渗透等宏观试验进行对比分析来确定。宏观试验结果表明：水泥加固红土的最大干密度增大、最优含水率减小,抗剪强度及抗剪强度指标增大,压缩系数和渗透系数减小。随水泥加入比例的增大和试样养护时间的延长,抗剪强度及抗剪强度指标逐渐增大,压缩系数和渗透系数逐渐减小,但抗剪强度、抗剪强度指标增大的程度和压缩系数、渗透系数减小的程度都逐渐变缓,粘聚力增大的程度大于内摩擦角增大的程度。水泥加入比例对红土各个力学特性的影响程度大于试样养护时间对红土各个力学特性的影响程度。
水泥加固红土的微结构特征可以通过扫描电镜观察对土样击实前后、加固前后、养护前后、剪切前后及压缩前后等不同情况下获得的不同放大倍数下的微结构图像进行对比分析提炼。扫描电镜试验表明：水泥加固红土具有密实性、胶结性、填充性、包裹性、孔隙性等微结构特征；对水泥加固红土的微结构图像进行数字化处理,提取了水泥加固红土的孔隙率和颗粒率等微结构特征参数。提取结果表明,经过击实、加固、养护及压缩后,水泥加固红土的孔隙率减小、颗粒率增大；随水泥加入比例的增大和试样养护时间的延长,水泥加固红土孔隙率逐渐减小、颗粒率逐渐增大；剪切后,孔隙率增大、颗粒率减小。
水泥加固红土力学特性的变化实质上在于其微结构的变化,而红土微结构的变化又取决于水泥与红土颗粒之间的相互作用。将水泥加固红土的力学特性、微结构特征及特征参数结合起来,通过水泥加固红土所体现出来的胶结作用、包裹作用和填充作用,从微结构的角度来解释水泥加固红土的机理。水泥加固红土力学特性的变化正是以上三种作用综合影响的结果。水泥加固红土最大干密度的增大和最优含水率的减小主要取决于包裹作用和填充作用；水泥加固红土抗剪强度和抗剪强度指标的增大及压缩性的减小主要取决于胶结作用；而水泥加固红土渗透性的减弱主要是由包裹作用和填充作用引起。
根据水泥不同加入比例、试样不同养护时间所获得的直剪试验数据,运用神经网络理论建立了神经网络模型。模型的输入层向量确定为水泥加入比例和试样养护时间两个影响因素,模型的输出层向量确定为粘聚力和内摩擦角两个抗剪强度指标,并对选定的样本数据进行归一化处理；通过试算,模型的隐层传递函数确定为正切函数tansig,模型的输出层传递函数确定为对数函数logsig,模型的隐层神经元数确定为5。根据模型构建层次,建立起水泥加固红土抗剪强度的神经网络模型,模型预测结果总体上令人满意。
This paper based on the problem of comprehensively analysis of soil reinforce、reinforcement mechanism、micro- structure、and neural network model, combining with the mechanical characteristics of laterite which is widely used in Yunnan can't completely satisfied engineering requirements, the problem "study on mechanical characteristics and neural network model of laterite-cement" was presented. Aiming at the typical laterite in Yunnan, this paper choosed cement as reinforced agent, and confirmed the mechanical characteristics and the microstructure characteristics of the laterite-cement through comparative study of the macro and micro test; Extracted the microstructure characteristic parameters of the laterite-cement by using image processing techniques; Illuminated that reinforcement mechanism of laterite-cement from the perspective of microstructure, combining with mechanical characteristics, microstructure characteristics and microstructure characteristic parameters; At last, the neural network model of shear strength of the laterite-cement was established by using neural network theory.
The mechanical characteristics of compaction、shear strength compression and permeability of the laterite-cement was confirmed through the contrastive analysis of compaction test、shear strength test、compression test and penetproportionn test, considering the cement proportion and the sample curing time. The macroscopic experiment result indicated:For the laterite-cement, the maximum dry density increased and the optimum moisture content decreased, the shearing strength and it's parameters increased, the compressibility coefficient and the permeability coefficient decreased. With increasing the cement proportion and extended the curing time, shear strength and it's parameters increased gradually, the compressibility coefficient and the permeability coefficient decreased gradually, but the extent will slow down gradually, the increased extend of cohesion force is greater than the extend of internal friction angle. For all kinds of mechanics feature of laterite, the incidence of cement proportion is greater than the incidence of the sample curing time.
The microstructure characteristic of the laterite-cement will be obtained by SEM, measuring and analyzing the microstructure images of different magnification factor before and after the condition of compaction reinforcement、curing、shear and compression. The SEM experiment result indicated:the the laterite-cement have the characteristic microstructure of close-grained、agglutinating、filling、enwrapping and porosity; Extracted the porosity rate and granular rate atc microstructure characteristic parameter through digitize image digital processing the microstructure of reinforced laterite. The extract result indicated:For the laterite-cement, the porosity rate decreased and the granular rate increased after compaction、reinforcement、curing and compression; With increasing the cement proportion and extended the curing time, the porosity rate decreased gradually, the granular rate decreased gradually; After shearing, the porosity rate increased and the granular rate decreased.
For the laterite-cement, the change of mechanical characteristics rest with the change of microstructure, and the change of microstructure rest with the interaction between cement and laterite granule. The reinforcement mechanism of laterite-cement was explained from the perspective of microstructure combining with mechanical characteristics, microstructure characteristics and microstructure characteristic parameters and through agglutinating、filling、enwrapping, the change of mechanical characteristics is the result of the combined influence of the three effects. For the laterite-cement, the increase of maximum dry density and the decrease of optimum moisture content rest with enwrapping and filling; the increase of the shearing strength and it's indicators and the decrease of compressibility rest with agglutinating; The decrease of permeability rest with enwrapping and filling.
Using the neural network theory, this paper established the neural network model according to the direct shear test's data which obtained through control cement different proportion and the sample different curing time. Make sure the cement proportion and curing time two factors as the input layer's vector quantity of model, make sure the cohesion force and internal friction angle two shear strength parameters as the output layer's vector quantity of model, and to make unitary processing for the selected specimen. The transfer function of hidden layer of the model is tangent function tansig and the transfer function of output layer of the model is log function logsin, the number of the hidden layer neuron of the model is 5. According to the arrangement of the model, the neural network model of shear strength of the laterite-cement is established, on the whole, the model's forecast result turn up trumps.
水泥加固红土的击实、抗剪强度、压缩、渗透等各个力学特性是在考虑水泥加入比例和试样养护时间的基础上,通过击实、直剪、压缩、渗透等宏观试验进行对比分析来确定。宏观试验结果表明：水泥加固红土的最大干密度增大、最优含水率减小,抗剪强度及抗剪强度指标增大,压缩系数和渗透系数减小。随水泥加入比例的增大和试样养护时间的延长,抗剪强度及抗剪强度指标逐渐增大,压缩系数和渗透系数逐渐减小,但抗剪强度、抗剪强度指标增大的程度和压缩系数、渗透系数减小的程度都逐渐变缓,粘聚力增大的程度大于内摩擦角增大的程度。水泥加入比例对红土各个力学特性的影响程度大于试样养护时间对红土各个力学特性的影响程度。
水泥加固红土的微结构特征可以通过扫描电镜观察对土样击实前后、加固前后、养护前后、剪切前后及压缩前后等不同情况下获得的不同放大倍数下的微结构图像进行对比分析提炼。扫描电镜试验表明：水泥加固红土具有密实性、胶结性、填充性、包裹性、孔隙性等微结构特征；对水泥加固红土的微结构图像进行数字化处理,提取了水泥加固红土的孔隙率和颗粒率等微结构特征参数。提取结果表明,经过击实、加固、养护及压缩后,水泥加固红土的孔隙率减小、颗粒率增大；随水泥加入比例的增大和试样养护时间的延长,水泥加固红土孔隙率逐渐减小、颗粒率逐渐增大；剪切后,孔隙率增大、颗粒率减小。
水泥加固红土力学特性的变化实质上在于其微结构的变化,而红土微结构的变化又取决于水泥与红土颗粒之间的相互作用。将水泥加固红土的力学特性、微结构特征及特征参数结合起来,通过水泥加固红土所体现出来的胶结作用、包裹作用和填充作用,从微结构的角度来解释水泥加固红土的机理。水泥加固红土力学特性的变化正是以上三种作用综合影响的结果。水泥加固红土最大干密度的增大和最优含水率的减小主要取决于包裹作用和填充作用；水泥加固红土抗剪强度和抗剪强度指标的增大及压缩性的减小主要取决于胶结作用；而水泥加固红土渗透性的减弱主要是由包裹作用和填充作用引起。
根据水泥不同加入比例、试样不同养护时间所获得的直剪试验数据,运用神经网络理论建立了神经网络模型。模型的输入层向量确定为水泥加入比例和试样养护时间两个影响因素,模型的输出层向量确定为粘聚力和内摩擦角两个抗剪强度指标,并对选定的样本数据进行归一化处理；通过试算,模型的隐层传递函数确定为正切函数tansig,模型的输出层传递函数确定为对数函数logsig,模型的隐层神经元数确定为5。根据模型构建层次,建立起水泥加固红土抗剪强度的神经网络模型,模型预测结果总体上令人满意。
This paper based on the problem of comprehensively analysis of soil reinforce、reinforcement mechanism、micro- structure、and neural network model, combining with the mechanical characteristics of laterite which is widely used in Yunnan can't completely satisfied engineering requirements, the problem "study on mechanical characteristics and neural network model of laterite-cement" was presented. Aiming at the typical laterite in Yunnan, this paper choosed cement as reinforced agent, and confirmed the mechanical characteristics and the microstructure characteristics of the laterite-cement through comparative study of the macro and micro test; Extracted the microstructure characteristic parameters of the laterite-cement by using image processing techniques; Illuminated that reinforcement mechanism of laterite-cement from the perspective of microstructure, combining with mechanical characteristics, microstructure characteristics and microstructure characteristic parameters; At last, the neural network model of shear strength of the laterite-cement was established by using neural network theory.
The mechanical characteristics of compaction、shear strength compression and permeability of the laterite-cement was confirmed through the contrastive analysis of compaction test、shear strength test、compression test and penetproportionn test, considering the cement proportion and the sample curing time. The macroscopic experiment result indicated:For the laterite-cement, the maximum dry density increased and the optimum moisture content decreased, the shearing strength and it's parameters increased, the compressibility coefficient and the permeability coefficient decreased. With increasing the cement proportion and extended the curing time, shear strength and it's parameters increased gradually, the compressibility coefficient and the permeability coefficient decreased gradually, but the extent will slow down gradually, the increased extend of cohesion force is greater than the extend of internal friction angle. For all kinds of mechanics feature of laterite, the incidence of cement proportion is greater than the incidence of the sample curing time.
The microstructure characteristic of the laterite-cement will be obtained by SEM, measuring and analyzing the microstructure images of different magnification factor before and after the condition of compaction reinforcement、curing、shear and compression. The SEM experiment result indicated:the the laterite-cement have the characteristic microstructure of close-grained、agglutinating、filling、enwrapping and porosity; Extracted the porosity rate and granular rate atc microstructure characteristic parameter through digitize image digital processing the microstructure of reinforced laterite. The extract result indicated:For the laterite-cement, the porosity rate decreased and the granular rate increased after compaction、reinforcement、curing and compression; With increasing the cement proportion and extended the curing time, the porosity rate decreased gradually, the granular rate decreased gradually; After shearing, the porosity rate increased and the granular rate decreased.
For the laterite-cement, the change of mechanical characteristics rest with the change of microstructure, and the change of microstructure rest with the interaction between cement and laterite granule. The reinforcement mechanism of laterite-cement was explained from the perspective of microstructure combining with mechanical characteristics, microstructure characteristics and microstructure characteristic parameters and through agglutinating、filling、enwrapping, the change of mechanical characteristics is the result of the combined influence of the three effects. For the laterite-cement, the increase of maximum dry density and the decrease of optimum moisture content rest with enwrapping and filling; the increase of the shearing strength and it's indicators and the decrease of compressibility rest with agglutinating; The decrease of permeability rest with enwrapping and filling.
Using the neural network theory, this paper established the neural network model according to the direct shear test's data which obtained through control cement different proportion and the sample different curing time. Make sure the cement proportion and curing time two factors as the input layer's vector quantity of model, make sure the cohesion force and internal friction angle two shear strength parameters as the output layer's vector quantity of model, and to make unitary processing for the selected specimen. The transfer function of hidden layer of the model is tangent function tansig and the transfer function of output layer of the model is log function logsin, the number of the hidden layer neuron of the model is 5. According to the arrangement of the model, the neural network model of shear strength of the laterite-cement is established, on the whole, the model's forecast result turn up trumps.
引文
[1]Tremblay H, Duchesne J, Locat J, Leroueil S. Influence of the nature of organic compounds on fine soil stabilization with cement[J]. Canadian Geotechnical Testing Journal,2002,39(3): 535-546
[2]Miller GA, Zaman M.Field and laboratory evaluation of cement kiln dust as a soil stabilizer[J]. Transportation Reasearch Record,2000, (1714):25-32
[3]Miller, P. Gand Tsugawa, P. R. Influence of soil type on stabilization with cement kiln dust[J]. Construction and Building Materials,2000,14(2):89-97
[4]Shenbaga R.Kaniraj, Vasant G.Havanagi.Compressive strength of cement stabilized fly ash-soilmixtures[J]. Cement and Concrete Research 1999, (29):673-677
[5]Okagbue,C O. Onyeobi, T. U. S. Potential of marble dust to stabilise red tropical soils for road construction[J]. Applied Clay Science,1999,53(3):371-380
[6]Shirazi H. Field and laboratory evaluation of the use of lime fly ash to replace soil cement as a base course[J]. Transportation Research Record,1999,1652:270-275
[7]Madina,J. Guida,H. N. Stabilization of lateritic soils with phosphoric acid[J]. International Journal of Rock Mechanics and Mining Science&Geomechanics Abstracts,1996, (6):272A
[8]Atton-Okinne. N. o. Lime treatment of laterite soils and gravels-revisited[J]. Construction and Building Materials,1995,9(5):283-287
[9]李晓全,马石城,屈畅姿等.水泥红粘土的强度试验研究[J].公路工程,2007,32(6)：49-52
[10]吴瑞潜,谢康和,陈先华.水泥和粉煤灰加固红黏土的试验研究[J].工业建筑,2006,36(7)：29-31
[11]庄心善,王功勋,朱瑞赓等.粉煤灰炉渣加固土的室内无侧限抗压强度试验研究[J].岩土工程学报,2005,27(8)：965-969
[12]黄春香.水泥-水玻璃加固软土研究[D].福州：福州大学,2002
[13]黄英,何发祥.一种新型土壤强固剂加固红土试验研究[J].工程勘察,2002,(3)：1-4
[14]黄英,符必昌.土壤强固剂加固红土的力学特性[J].水文地质工程地质,2002,(5)：4-7
[15]张明.水泥加固土工程性质的试验研究与分析[D].太原：太原理工大学,2001
[16]黄新,胡同安.工业废石膏与水泥配合加固软土地基[J].建筑技术,2001,32(3)：161-163
[17]杨重存.黄土固化技术在公路工程中的应用及试验研究[D].西安：西安公路交通大学,2000
[18]黄殿瑛.硅粉(SF)对水泥土性能影响的研究[D].武汉：中南工业大学,1994
[19]高国瑞.细粒土结构专门术语、概念和分类命名的初步方案[J].水文地质工程地质,1986(1)： 8-16
[20]石玉成,李兰,刘红玫.黄土的震陷性与其微结构特征的关系研究[J],西北地震学报,2002,24(2)：129-134
[21]刘红玫,王兰民.饱和黄土液化的孔隙微结构特征[J].西北地震学报,2002,24(2)：135-139
[22]胡瑞林,官国琳,李向东等.黄土压缩变形的微结构效应[J].水文地质工程地质,1998,(3)： 30-35
[23]胡瑞林、官国琳、李向全等.黄土湿陷性的微结构效应[J].工程地质学报,1999,7(2)：161-167
[24]高国瑞.黄土湿陷变形的结构理论[J].岩土工程学报,1990,(4)
[25]高国瑞.黄土显微结构分类与湿陷性[J].中国科学,1980,(12)
[26]高国瑞.中国黄土的微结构[J].科学通报,1980,(20)
[27]高国瑞.兰州黄土显微结构和湿陷机理探讨[J].兰州大学学报,1979,(1)
[28]王永炎等.中国黄土的微结构及其在时代和区域上的变化[J].科学通报,1982,(2)
[29]李生林,秦素娟,薄遵昭等.中国膨胀土工程地质研究[D].南京：江苏科技出版社,1992
[30]施斌,李生林.击实膨胀土微结构与工程特性的关系[J].岩土工程学报,1988,10(6)：80-87
[31]高国瑞.膨胀土微观结构特征的研究[J],工程勘察,1981,(5)
[32]罗鸿喜.陨县膨胀土的矿物成分及微结构研究,工程勘察,1981,(5)
[33]孔令伟,吕海波等.海口某海域软土工程特性的微观机制浅析[J].岩土力学,2002,23(1)：36-40
[34]李榴芬.软土微结构制样的一点体会[J].西部探矿工程,2000,12(5)：22-23
[35]李向全,胡瑞林,张莉.软土固结中的微结构变化特征[J].地学前缘,2000,7(1)：147-152
[36]王常明.海积软土堆载预压试验监测结果分析与加固效果评价[J].吉林地质,1998,17(3)：70-74
[37]夏佳,陈新民,严三保等.现代河沿滩软土固结与压缩微结构探析[J].岩土工程学报,1997,19(5)：67-73
[38]李青云.深圳机场残积土的微观试验研究[J].土工基础,1994,8(2)：20-26
[39]金克盛.昆明红土的固化特性及微观结构图像特征参数研究[D].昆明：昆明理工大学,2005
[40]周训华,廖义玲.红粘土颗粒之间结构连结的胶体化学特征[J].贵州工业大学学报(自然科学版),2004,33(1)：26-29
[41]谭罗荣,孔令伟.某类红粘土的基本特性与微观结构模型[J].岩土工程学报,2001,23(4)：458-462
[42]孔令伟,罗鸿禧.游离氧化铁形态转化对红粘土工程性质的影响[J].岩土力学,1993,14(4)：25-29
[43]高国瑞.中国红土的微结构和工程性质[J].岩土工程学报,1985,7(5)：10-21
[44]张春雷,淤泥固化土力学性质及固化机理研究[D].南京：河海大学,2001
[45]黄新,周国钧.水泥加固土硬化机理初探[J].岩土工程学报,1994,16(1)：63-65
[46]刘顺妮,林宗寿,陈云波.高含水率粘土固化材料的研究,岩土工程学报,1988,20(4)：72-75
[47]胡昕,孙秋,洪宝宁等.某粘性土压缩性与微结构形态之间的关系[J].中国港湾建设,2006,(5)：16-18,32
[48]McCulloch. W. S, Pitts. W. A logical calculus of the ideas immanent in Nervous Activity[J]. Bull Math. Biophysics,1943,5:115-133
[49]Hopfield JJ. Neural networks and physics systems with emergent collective computertation abilities. Proc.Natl Acad Sci USA.1982,79:2554-2558.
[50]从爽.面向Matlab工具箱的神经网络理论与应用[M].中国科学技术大学出版社,1998：45-46
[51]曾洪飞.RBF神经网络预测土的物理力学指标初探[J].建材技术与应用,2007,9：7-9
[52]张高峰,梁宾桥,谌会芹.用BP神经网络预测土抗剪强度指标c,(?)[J].岩土工程技术,2006,20(1)：36-38,44
[53]龚羊庆,黄英,金克盛.红土抗剪强度指标及其BP网络模型研究[J].昆明理工大学学报,2004,29(6)：5-8,14
[54]许传华,房定旺.边坡稳定性分析中工程岩体抗剪强度参数选取的神经网方法[J].岩石力学与工程学报,2002,21(6)：858-862
[55]骆以道.一种非饱和土抗剪强度的预测方法[J].大坝观测与土工测试,2001,25(6)：41-44
[56]S.Malomo(郑秉仁,屈儒敏译). “红土”一词在工程地质领域中的用法[A]/国外工程地质研究[D].北京：地质出版社,1986：404-409
[57]Samuel Akinlabiola.Mineralogical Properties of some Ningerian Residual Soilin with Buiding Problems[J]. Eng.Geol,1980, (15):1-2
[58]中华人民共和国水利部.土工试验规程SL239-1999[M].水利电力出版社,2000
[59]杨进良.土力学[M].北京：中国水利水电出版社,2006：13-15
[60]Tatsuka Okumura. Deepmixing Method as a Chemical Soil Improvement[M]. Proc.of the Sino Japan Joint Symposium on Improvement of Weak Groun,1989
[61]柳墩利,赵有明.击实延迟时间对水泥改良土压实系数影响的研究[J].铁道建筑,2008,(8),91-94
[62]杨进良.土力学[M].北京：中国水利水电出版社,2006：94-95
[63]景晓军,周贤伟,付娅丽.图像处理技术及其应用[M].北京：国防工业出版社,2005.8
[64]张登良.加固土原理[M].北京：人民交通出版社,1990
[65]郑颖人,沈珠江,龚晓楠.岩土塑性力学原理[M].北京：中国建筑工业出版社,2003
[66]常斌,李宁.BP网络非线性系数取值研究及其在岩土工程中的应用[J].西安理工大学学报,2003,19(1)：30-94
[67]刘勇健,沈军,刘义健.人工神经网络在水泥加固土力学性能预测中的应用[J].岩土力学,2001,22(3)：330-333
[68]顾成权,孙艳.土体内聚力随含水量、粘粒含量及干密度变化关系探讨[J].水文地质工程地质,2005,32(1)：34-36
[69]Rumelhart D., McClelland J. Parallel Distributed processing, Exploproportionns in the Microstructure of cognition. Cambridge:Bradford Books, MIT Press. pp. vol-vo2.1986
[70]Robert Hecht-Nielsen. Neural Network-A Break-through in Information Prcocessing Technology. HNC, Inc.1989
[71]葛哲学,孙志强.神经网络理论与MATLABR2007实现[M].北京：电子工业出版社,2007
[72]赵振宁.模糊理论和神经网络的基础与应用[M].北京：清华大学出版社,1996
[73]何发祥,黄英.用BP网络求解土体的导热系数[J].岩土力学,2000,21(1)：84-87
[2]Miller GA, Zaman M.Field and laboratory evaluation of cement kiln dust as a soil stabilizer[J]. Transportation Reasearch Record,2000, (1714):25-32
[3]Miller, P. Gand Tsugawa, P. R. Influence of soil type on stabilization with cement kiln dust[J]. Construction and Building Materials,2000,14(2):89-97
[4]Shenbaga R.Kaniraj, Vasant G.Havanagi.Compressive strength of cement stabilized fly ash-soilmixtures[J]. Cement and Concrete Research 1999, (29):673-677
[5]Okagbue,C O. Onyeobi, T. U. S. Potential of marble dust to stabilise red tropical soils for road construction[J]. Applied Clay Science,1999,53(3):371-380
[6]Shirazi H. Field and laboratory evaluation of the use of lime fly ash to replace soil cement as a base course[J]. Transportation Research Record,1999,1652:270-275
[7]Madina,J. Guida,H. N. Stabilization of lateritic soils with phosphoric acid[J]. International Journal of Rock Mechanics and Mining Science&Geomechanics Abstracts,1996, (6):272A
[8]Atton-Okinne. N. o. Lime treatment of laterite soils and gravels-revisited[J]. Construction and Building Materials,1995,9(5):283-287
[9]李晓全,马石城,屈畅姿等.水泥红粘土的强度试验研究[J].公路工程,2007,32(6)：49-52
[10]吴瑞潜,谢康和,陈先华.水泥和粉煤灰加固红黏土的试验研究[J].工业建筑,2006,36(7)：29-31
[11]庄心善,王功勋,朱瑞赓等.粉煤灰炉渣加固土的室内无侧限抗压强度试验研究[J].岩土工程学报,2005,27(8)：965-969
[12]黄春香.水泥-水玻璃加固软土研究[D].福州：福州大学,2002
[13]黄英,何发祥.一种新型土壤强固剂加固红土试验研究[J].工程勘察,2002,(3)：1-4
[14]黄英,符必昌.土壤强固剂加固红土的力学特性[J].水文地质工程地质,2002,(5)：4-7
[15]张明.水泥加固土工程性质的试验研究与分析[D].太原：太原理工大学,2001
[16]黄新,胡同安.工业废石膏与水泥配合加固软土地基[J].建筑技术,2001,32(3)：161-163
[17]杨重存.黄土固化技术在公路工程中的应用及试验研究[D].西安：西安公路交通大学,2000
[18]黄殿瑛.硅粉(SF)对水泥土性能影响的研究[D].武汉：中南工业大学,1994
[19]高国瑞.细粒土结构专门术语、概念和分类命名的初步方案[J].水文地质工程地质,1986(1)： 8-16
[20]石玉成,李兰,刘红玫.黄土的震陷性与其微结构特征的关系研究[J],西北地震学报,2002,24(2)：129-134
[21]刘红玫,王兰民.饱和黄土液化的孔隙微结构特征[J].西北地震学报,2002,24(2)：135-139
[22]胡瑞林,官国琳,李向东等.黄土压缩变形的微结构效应[J].水文地质工程地质,1998,(3)： 30-35
[23]胡瑞林、官国琳、李向全等.黄土湿陷性的微结构效应[J].工程地质学报,1999,7(2)：161-167
[24]高国瑞.黄土湿陷变形的结构理论[J].岩土工程学报,1990,(4)
[25]高国瑞.黄土显微结构分类与湿陷性[J].中国科学,1980,(12)
[26]高国瑞.中国黄土的微结构[J].科学通报,1980,(20)
[27]高国瑞.兰州黄土显微结构和湿陷机理探讨[J].兰州大学学报,1979,(1)
[28]王永炎等.中国黄土的微结构及其在时代和区域上的变化[J].科学通报,1982,(2)
[29]李生林,秦素娟,薄遵昭等.中国膨胀土工程地质研究[D].南京：江苏科技出版社,1992
[30]施斌,李生林.击实膨胀土微结构与工程特性的关系[J].岩土工程学报,1988,10(6)：80-87
[31]高国瑞.膨胀土微观结构特征的研究[J],工程勘察,1981,(5)
[32]罗鸿喜.陨县膨胀土的矿物成分及微结构研究,工程勘察,1981,(5)
[33]孔令伟,吕海波等.海口某海域软土工程特性的微观机制浅析[J].岩土力学,2002,23(1)：36-40
[34]李榴芬.软土微结构制样的一点体会[J].西部探矿工程,2000,12(5)：22-23
[35]李向全,胡瑞林,张莉.软土固结中的微结构变化特征[J].地学前缘,2000,7(1)：147-152
[36]王常明.海积软土堆载预压试验监测结果分析与加固效果评价[J].吉林地质,1998,17(3)：70-74
[37]夏佳,陈新民,严三保等.现代河沿滩软土固结与压缩微结构探析[J].岩土工程学报,1997,19(5)：67-73
[38]李青云.深圳机场残积土的微观试验研究[J].土工基础,1994,8(2)：20-26
[39]金克盛.昆明红土的固化特性及微观结构图像特征参数研究[D].昆明：昆明理工大学,2005
[40]周训华,廖义玲.红粘土颗粒之间结构连结的胶体化学特征[J].贵州工业大学学报(自然科学版),2004,33(1)：26-29
[41]谭罗荣,孔令伟.某类红粘土的基本特性与微观结构模型[J].岩土工程学报,2001,23(4)：458-462
[42]孔令伟,罗鸿禧.游离氧化铁形态转化对红粘土工程性质的影响[J].岩土力学,1993,14(4)：25-29
[43]高国瑞.中国红土的微结构和工程性质[J].岩土工程学报,1985,7(5)：10-21
[44]张春雷,淤泥固化土力学性质及固化机理研究[D].南京：河海大学,2001
[45]黄新,周国钧.水泥加固土硬化机理初探[J].岩土工程学报,1994,16(1)：63-65
[46]刘顺妮,林宗寿,陈云波.高含水率粘土固化材料的研究,岩土工程学报,1988,20(4)：72-75
[47]胡昕,孙秋,洪宝宁等.某粘性土压缩性与微结构形态之间的关系[J].中国港湾建设,2006,(5)：16-18,32
[48]McCulloch. W. S, Pitts. W. A logical calculus of the ideas immanent in Nervous Activity[J]. Bull Math. Biophysics,1943,5:115-133
[49]Hopfield JJ. Neural networks and physics systems with emergent collective computertation abilities. Proc.Natl Acad Sci USA.1982,79:2554-2558.
[50]从爽.面向Matlab工具箱的神经网络理论与应用[M].中国科学技术大学出版社,1998：45-46
[51]曾洪飞.RBF神经网络预测土的物理力学指标初探[J].建材技术与应用,2007,9：7-9
[52]张高峰,梁宾桥,谌会芹.用BP神经网络预测土抗剪强度指标c,(?)[J].岩土工程技术,2006,20(1)：36-38,44
[53]龚羊庆,黄英,金克盛.红土抗剪强度指标及其BP网络模型研究[J].昆明理工大学学报,2004,29(6)：5-8,14
[54]许传华,房定旺.边坡稳定性分析中工程岩体抗剪强度参数选取的神经网方法[J].岩石力学与工程学报,2002,21(6)：858-862
[55]骆以道.一种非饱和土抗剪强度的预测方法[J].大坝观测与土工测试,2001,25(6)：41-44
[56]S.Malomo(郑秉仁,屈儒敏译). “红土”一词在工程地质领域中的用法[A]/国外工程地质研究[D].北京：地质出版社,1986：404-409
[57]Samuel Akinlabiola.Mineralogical Properties of some Ningerian Residual Soilin with Buiding Problems[J]. Eng.Geol,1980, (15):1-2
[58]中华人民共和国水利部.土工试验规程SL239-1999[M].水利电力出版社,2000
[59]杨进良.土力学[M].北京：中国水利水电出版社,2006：13-15
[60]Tatsuka Okumura. Deepmixing Method as a Chemical Soil Improvement[M]. Proc.of the Sino Japan Joint Symposium on Improvement of Weak Groun,1989
[61]柳墩利,赵有明.击实延迟时间对水泥改良土压实系数影响的研究[J].铁道建筑,2008,(8),91-94
[62]杨进良.土力学[M].北京：中国水利水电出版社,2006：94-95
[63]景晓军,周贤伟,付娅丽.图像处理技术及其应用[M].北京：国防工业出版社,2005.8
[64]张登良.加固土原理[M].北京：人民交通出版社,1990
[65]郑颖人,沈珠江,龚晓楠.岩土塑性力学原理[M].北京：中国建筑工业出版社,2003
[66]常斌,李宁.BP网络非线性系数取值研究及其在岩土工程中的应用[J].西安理工大学学报,2003,19(1)：30-94
[67]刘勇健,沈军,刘义健.人工神经网络在水泥加固土力学性能预测中的应用[J].岩土力学,2001,22(3)：330-333
[68]顾成权,孙艳.土体内聚力随含水量、粘粒含量及干密度变化关系探讨[J].水文地质工程地质,2005,32(1)：34-36
[69]Rumelhart D., McClelland J. Parallel Distributed processing, Exploproportionns in the Microstructure of cognition. Cambridge:Bradford Books, MIT Press. pp. vol-vo2.1986
[70]Robert Hecht-Nielsen. Neural Network-A Break-through in Information Prcocessing Technology. HNC, Inc.1989
[71]葛哲学,孙志强.神经网络理论与MATLABR2007实现[M].北京：电子工业出版社,2007
[72]赵振宁.模糊理论和神经网络的基础与应用[M].北京：清华大学出版社,1996
[73]何发祥,黄英.用BP网络求解土体的导热系数[J].岩土力学,2000,21(1)：84-87