共查询到19条相似文献，搜索用时 171 毫秒
1.
对于加筋支挡结构的设计,神经网络模型不同于基于凝聚力基础之上的半经验公式,它不需要主观的人为假设,而是模拟人脑思维,通过数据样本的学习来获得预测结果.BP神经网络是对非线性可微分函数进行权值训练的分层网络.文中采用BP网络对给定极限荷载下砂土挡墙筋条的临界长度进行预测,试验点几乎分布在预测曲线附近,说明网络学习是成功的.对不同筋材的极限荷载与临界长度的关系进行了对比分析,表明筋材的弹性模量及筋材与填料之间的摩擦系数对加筋性能有着重要的影响. 相似文献
2.
《岩土力学》2017,(11)
由于固结沉降、降雨或地震等作用,在软土或新老地基结合处,不均匀沉降现象是一种常见的工程病害,采用加筋土结构来调整不均匀沉降是工程上常用的处理措施。然而,现阶段关于加筋土结构在不均匀沉降条件下筋材变形规律的理论研究尚少,且工程实践往往先于理论研究。为完善并简化加筋土结构的设计理论,以加筋土结构中筋材变形为研究对象,在基于抛物索理论的基础上,提出了一种在不均匀沉降下加筋土结构筋材变形的计算方法。通过室内模型试验并结合理论分析初步验证了该计算方法的可行性和准确性。该方法计算简便,不需要反复的数值模拟,计算结果能够满足实际工程的精度需要,对进一步完善加筋土结构设计的计算理论具有一定的意义。 相似文献
3.
《岩土力学》2020,(Z1)
土工合成材料加筋土柔性桥台复合结构(GRS-IBS)是一种可以实现桥梁一体化设计施工的新型技术,针对该种结构的抗震性能的研究还不多。通过一组振动台缩尺模型试验,研究GRS-IBS结构的抗震性能,研究结果表明,GRS-IBS结构具有良好的抗震性能,在强震作用下(地震波的峰值加速度达到1.0g)能保持良好的整体稳定性能,仅在桥台面层顶部出现较小的外倾变形(最大变形约为桥台高度的2%)和桥头处轻微的差异沉降;由于地震波的方向性和波形不对称,两侧桥台受到不同的指向临空面的惯性力作用,桥跨结构的存在可使其附近区域的加筋土体所受的惯性力通过桥跨结构传递协调趋于一致;减小桥台的加筋间距有利于提高加筋桥台的抗震性能,主要表现在减小结构变形及分担筋材轴力方面。 相似文献
4.
差异沉降是造成路面破坏、影响行车速度和引发交通事故的主要因素之一,加筋土技术作为经济性良好的治理措施被广泛应用。为研究预应力加筋土垫层在差异沉降下的变形规律和沉降控制性能,设计了加筋土垫层和预应力加筋土垫层差异沉降对比模型试验。通过对比试验揭示了预应力加筋土垫层对差异沉降控制的有益效果。试验发现:①预应力加筋垫层可以有效地降低差异沉降。②预应力加筋垫层有利于调节软硬路基交界面附近的“断崖式”差异沉降。③预应力加筋垫层对能量的耗散和传递能力更好。④附加应力从垫层传递到路基顶部时发生应力叠加,在该处呈现增大突变趋势。本文研究成果可为预应力加筋土结构在差异沉降频发路段的工程应用和控制性能研究提供借鉴和依据。 相似文献
5.
根据加筋方式的不同,纤维加筋可分为定向分布的纤维加筋和随机分布的纤维加筋两大类。本文针对这两类加筋方式,分别综述了纤维加筋技术在土体抗液化方面的相关研究进展,同时对纤维加筋土抗液化性能的主要影响因素进行了分析。既有针对纤维加筋土进行的动三轴试验、扭剪试验及模型试验等均表明,纤维加筋技术能有效地增强土体的抗液化强度,同时减小液化所引起的变形,是一种具有广泛应用前景的抗液化措施。 相似文献
6.
7.
地下工程围岩分类的神经网络可视化评价 总被引：1，自引：0，他引：1
围岩分类是地下工程技术基础研究的重要课题之一。影响围岩类别的因素有很多，并且具有高度的随机性和模糊性。神经网络方法具有自组织、自学习和高度非线性映射的能力，并且既能考虑定量因素又能考虑定性因素，因此，在围岩分类的应用方面，神经网络具有广泛的前景。文章基于改进的BP神经网络的一般原理，依据有关地下工程围岩的分类标准，选取岩体结构、岩石饱和单轴抗压强度、岩体结构面、岩石纵波速度作为围岩分类的评价指标，利用MATLAB语言构建了可视化的围岩分类神经网络模型，并收集了大量的工程资料对网络进行训练和检验。结果表明，网络的预测结果与实际结果比较一致。证明神经网络是能够在工程岩体分类方面得到推广应用的。 相似文献
8.
草根加筋土的室内三轴试验研究 总被引：10，自引：0，他引：10
用室内三轴试验方法研究了草根加筋土的应力-应变及强度特性,探讨了在不同加筋情况下草根加筋土抗剪强度指标的变化规律,分析了素土和草根加筋土的变形破坏模式以及筋材在土体剪切过程中的阻抗机理。试验结果表明：（1）草根加筋土的强度和抵抗变形的能力较素土有显著的增强;（2）对于抗剪强度指标,与素土相比草根加筋土的内摩擦角变化较小（相对变化率绝对值在11 %以内）,但黏聚力增长较大（最大可达9倍）;（3）在草根加筋层数一定的情况下,随着加筋量的增加,草根加筋土的主应力差值、抗剪强度值以及黏聚力值呈现先增加后降低的趋势,亦即加筋层数相同时存在最佳含根量。本研究结果对于深入认识植被护坡机理和合理选择植被密度具有指导意义。 相似文献
9.
纤维加筋土工程性质研究进展 总被引：7，自引：0，他引：7
纤维加筋是一种新型的土体改良技术,是指将分散的纤维丝均匀掺入土体中,达到提高土体的工程力学性能的目的。本文根据国内外近20a来在该领域取得的成果,着重对纤维加筋材料、纤维加筋土的工程性质及加筋机理等方面的研究进展进行了介绍。在纤维加筋材料方面,主要有人工合成纤维和自然纤维两种; 在纤维加筋土工程性质方面,纤维加筋能有效提高土体的抗剪、抗压、抗拉强度和承载力,增加土体的破坏韧性和渗透性,降低膨胀土的胀缩性和裂隙发育程度; 在纤维加筋机理方面,目前主要认为纤维的加筋效果主要取决于纤维-土界面的力学作用,即界面黏聚力和摩擦力。最后,针对目前纤维加筋土的研究不足,提出了今后该领域的研究重点和方向,主要包括: 开发简单可行的纤维土施工器具和工艺、开发更专业的纤维产品和降低纤维成本、开展大尺度模型试验和现场试验、纤维-土界面作用机理研究、纤维加筋土的破坏和失效机理研究、纤维加筋土的力学模型及计算理论研究、纤维加筋土的动力学特性研究等。 相似文献
10.
11.
12.
Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model 总被引：2，自引：0，他引：2
A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, EV).
The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding. 相似文献
13.
14.
The mineral resource estimation requires accurate prediction of the grade at location from limited borehole information. It plays the dominant role in the decision-making process for investment and development of various mining projects and hence become an important and crucial stage. This paper evaluvates the use of two distinct artificial neural network (ANN)-based models, general regression neural network (GRNN) and multilayer perceptron neural network (MLP NN), to improve the grade estimation from Koira iron ore region in Sundargarh district, Odisha. ANN-based models capture the inherent complex structure of mineral deposits and provide a reliable generalization of the iron grade. The ANN-based approach does not require any preliminary geological study and is free from any statistical assumption on the raw data before its application. The GRNN is a one-pass learning algorithm and does not require any iterative procedure for training less complex structure and requires only one learning parameter for optimization. In this investigation, the spatial coordinates and multiple lithological units were taken as input variables and the iron grade was taken as the output variable. The comparative analysis of these models has been carried out and the results obtained were validated with traditional geostatistical method ordinary kriging (OK). The GRNN model outperforms the other methods, i.e. MLP and OK, with respect to generalization and predictability of the grades at an un-sampled location. 相似文献
15.
C. Kayadelen 《国际地质力学数值与分析法杂志》2008,32(9):1087-1106
Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
16.
Prediction of pile settlement using artificial neural networks based on standard penetration test data 总被引：4，自引：0，他引：4
F. Pooya Nejad Mark B. Jaksa M. Kakhi Bryan A. McCabe 《Computers and Geotechnics》2009,36(7):1125-1133
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions. 相似文献
17.
自加筋土出现以来,由面板、筋材、填土组成的加筋土挡墙被广泛研究并应于道路、铁路等土建工程中。填土的压实对加筋土挡墙的变形、土压力及筋材拉力等影响显著。为研究填土相对密实度对加筋土挡墙的影响,进行了3组不同相对密实度的离心模型试验,通过试验数据分析得到以下结论：相对密实度越大,墙体变形越小,特别是加载期变形量;压实可增大面板附近土体约束,使水平土压力大于设计值;试验挡墙设计较保守,筋材填土界面摩擦系数小于设计值;筋材面板之间连接拉力分析表明,连接拉力实测值小于测试土压力。 相似文献
18.
19.
针对红板岩材料在岩土工程中所表现的大量模糊的和不确定的因素等特点,基于人工神经网络的学习能力,借助于室内岩石力学试验,进行了对该材料的力学本构特性进行了神经网络模拟研究,提出了隐式本构模型的思想和方法,并通过该方法对该岩石的流变试验结果进行学习,获得了以网络权值结构保存的力学特性知识,由此得到了表征红板岩应力应变本构关系的隐式本构模型。应用结果表明,该方法对岩土类材料本构关系的模拟研究具有很好的应用前景。 相似文献