共查询到20条相似文献，搜索用时 31 毫秒
1.
深水系泊系统及其系泊基础的研发已成为国际海洋工程领域的前沿和关键课题,拖曳锚因其在承载力和深水安装中的诸多优势而具有良好的发展前景。在拖曳锚安装过程中,锚板与安装缆绳在系缆点处发生复杂的相互作用,分析二者之间的相互作用可等价为分析系缆点拖曳力的大小,研究拖曳锚与安装缆绳相互作用力的计算方法对认识锚板在海床土中的受力以及确定拖曳锚安装船的吨位具有重要指导意义。分别基于嵌入缆绳和拖曳锚在海床土中的力学模型,首次推导出适用于黏性土和无黏性土的系缆点拖曳力表达式,并开展模型试验验证,对比两类拖曳力计算方法的精度和稳定性,结果表明,两类计算方法均能合理预测系缆点拖曳力,但基于拖曳锚受力模型获得的表达式更为精确和稳定;通过参数考察,探究系缆点拖曳力预测模型中各参数的影响效应;经过与试验对比,给出关键参数的建议取值,该取值对研究结构与土体的相互作用问题具有重要参考价值。 相似文献
2.
吸力式筒形基础在海洋工程中已获得越来越广泛地应用,其安装过程的数值模拟对指导工程实践具有重要意义。在大型通用有限元软件ABAQUS平台上建立了二维轴对称模型,基于ALE（任意拉格朗日-欧拉法）技术模拟了黏土中吸力筒的大变形沉贯过程。模拟过程利用了子程序VUFIELD控制土体的不排水抗剪强度和弹性模量随土体深度变化。参照离心机试验及理论计算,对模型进行验证。利用已验证模型分析不同吸力下沉贯阻力、土塞高度,并讨论了筒壁摩擦特性。数值计算结果表明,ALE技术能有效地模拟吸力筒贯入过程,避免网格畸变。贯入方式对贯入阻力影响很大,吸力式贯入阻力明显低于压力式贯入阻力。进一步研究发现,随着最终吸力值的增大,沉贯阻力会显著降低,土塞高度会显著提高。对内壁摩擦特性的研究表明,内壁摩擦阻力是导致沉贯阻力改变的主要因素,并且相比吸力式贯入方式,筒壁摩擦特性会对压力式贯入造成更大的影响。 相似文献
3.
4.
Souhir Ellouze Zeineb Bensalem Mohamed Nadhir Znaidi 《Geomechanics and Geoengineering》2017,12(2):73-85
This paper addresses the installation effects of stone columns in soft soils. Focus is made on the lateral expansion of stone material using the vibro displacement and substitution techniques by means of numerical simulations. The behaviour of reinforced soil after stone column installation is investigated to show how the properties of soft soils can be improved prior to final loading. The effect of such an improvement on the prediction of reinforced soil settlement is evaluated. The axisymmetric unit cell model (UCM) served for the comparison between numerical predictions made by the Mohr-Coulomb and hardening soil constitutive laws adopted for the soft soil. An equivalent group of end bearing columns model was investigated in the axisymmetric condition to predict the settlement of reinforced soil by adopting the Mohr-Coulomb constitutive model for soft clay. The reduction of settlements predicted by the unit cell and group of columns models, due the improvement of the Young’s modulus of soft clay, were compared. It is concluded that a significant reduction of settlement is expected when the group of columns model is considered. 相似文献
5.
A comparison of logistic regression-based models of susceptibility to landslides in western Colorado,USA 总被引：4，自引：2，他引：2
The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %). 相似文献
6.
7.
Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available. 相似文献
8.
With the rising needs of better prediction of the load-displacement performance of grouted anchors in an era of developing large-scale underground infrastructures,the existing methods in literature lack an accurate analytical model for the real-life projects or rigorous understanding of the parameters such as grouting pressures.This paper proposes Fast ICA-MARS as a novel data-driven approach for the prediction of the load-displacement performance of uplift-resisting grouted anchors.The hybrid and data-driven Fast ICA-MARS approach integrates the multivariate adaptive regression splines(MARS)technique with the Fast ICA algorithm which is for Independent Component Analysis(ICA).A database of 4315 observations for 479 different anchors from 7 different projects is established.The database is then used to train,validate and compare the Fast ICA-MARS approach with the classical MARS approach.The developed Fast ICA-MARS model can provide more accurate predictions than MARS.Moreover,the developed Fast ICA-MARS model is easy to interpret since the evaluation of the parameter importance of the independent components can be conducted along with the considerations of the correlations with the original variables.It is noteworthy to point out that the grouting pressures play a central role in the proposed model,which is considered of paramount importance in engineering practices but has not been properly taken into account in any prior analytical or empirical predictive models for the load-displacement relationships. 相似文献
9.
下限分析有限单元法将下限定理这一数学变分问题转化为一个数学规划问题,克服了人为构造可静应力场的困难,在实际工程中具有广阔的应用前景。通过有限元离散得到的非线性下限规划模型中包含大量的优化变量与约束条件,常规优化算法难以求解。为此,在分析非线性下限规划模型自身特点的基础上,引入可行弧技术和Wolfe非精确搜索技术改进其优化求解效率。算例分析表明,基于可行弧技术和Wolfe非精确搜索技术,下限分析有限单元法优化求解程序的收敛速度和步长搜索效率得到明显的提升,并且其数值稳定性良好、计算精度较高,可以较好地适应实际工程问题的计算。 相似文献
10.
Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model. 相似文献
11.
Binh Thai Pham Dieu Tien Bui Indra Prakash 《Geotechnical and Geological Engineering》2017,35(6):2597-2611
In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alternating Decision Trees (BADT), Logistic Regression (LR), and J48 Decision Trees (J48DT) for landslide susceptibility mapping at part of the Uttarakhand State (India). The BADT method has been proposed in the present study which is a novel hybrid machine learning ensemble approach of bagging ensemble and alternating decision trees. The J48DT is a relative new machine learning technique which has been applied only in few landslide studies, and the LR is known as a popular landslide susceptibility model. For the model studies, a spatial database of 930 historical landslide events and 15 landslide affecting factors have been collected and analyzed. This database has been used to build and validate the landslide models namely BADT, LR and J48DT Predictive capability of these models has been validated and compared using statistical analyzing methods and Receiver Operating Characteristic (ROC) curve. Results show that these three landslide models (BADT, LR and J48DT) performed well with the training dataset. However, using the validation dataset the BADT model has the highest prediction capability, followed by the LR model, and the J48DT model, respectively. This indicates that the BADT is a promising method which can be used for landslide susceptibility assessment also for other landslide prone areas. 相似文献
12.
Mohamed A. Shahin 《Geomechanics and Geoengineering》2015,10(2):109-125
In this paper, the feasibility of using evolutionary computing for solving some complex problems in geotechnical engineering is investigated. The paper presents a relatively new technique, i.e. evolutionary polynomial regression (EPR), for modelling three practical applications in geotechnical engineering including the settlement of shallow foundations on cohesionless soils, pullout capacity of small ground anchors and ultimate bearing capacity of pile foundations. The prediction results from the proposed EPR models are compared with those obtained from artificial neural network (ANN) models previously developed by the author, as well as some of the most commonly available methods. The results indicate that the proposed EPR models agree well with (or better than) the ANN models and significantly outperform the other existing methods. The advantage of EPR technique over ANNs is that EPR generates transparent and well-structured models in the form of simple and easy-to-use hand calculation formulae that can be readily used by practising engineers. 相似文献
13.
Prediction of time to slope failure: a general framework 总被引：4，自引：1，他引：3
A. Federico M. Popescu G. Elia C. Fidelibus G. Internò A. Murianni 《Environmental Earth Sciences》2012,66(1):245-256
The prediction of time to slope failure (TSF) is a goal of major importance for both landslide researchers and practitioners.
A reasonably accurate prediction of TSF allows human losses to be avoided, damages to property to be reduced and adequate
countermeasures to be designed. A pure “phenomenological” approach based on the observation and interpretation of the monitored
data is generally employed in TSF prediction. Such an approach infers TSF mainly from the ground surface displacements using
regression techniques based on empirical functions. These functions neglect the rheological soil parameters in order to reduce
the prediction uncertainties. This paper presents an overlook of the methods associated with this approach and proposes a
unique expression encompassing most of the previously proposed equations for TSF prediction, thus offering a general framework
useful for comparisons between different methods. The methods discussed in this paper provide an effective tool, and sometimes
the only tool, for TSF prediction. The fundamental problem is always one of data quality. A full confidence in all assumptions
and parameters used in the prediction model is rarely, if ever, achieved. Therefore, TSF prediction models should be applied
with care and the results interpreted with caution. Documented case studies represent the most useful source of information
to calibrate the TSF prediction models. 相似文献
14.
In foundation engineering practice, pile driving is often used as an efficient method to install piles. While large distortions take place along the pile shaft during the installation, the zone around the pile toe experiences compression. In an attempt to fully understand the build up of resistance when driving piles, it is desirable to model the driving process and the corresponding soil behaviour. The non-linear dynamic analysis of this problem is challenging, given the large deformation that develops together with the associated changes in soil properties. Some numerical methods offer the possibility of handling large material movements by utilising Lagrangian and Eulerian frames of references. However, few of these methods are capable of tracing the material displacement, such as the Material Point Method (MPM). Early implementation of MPM assumes that the mass is concentrated at the material points, which causes noise in the solution. Later implementations assign a spatial domain to the material points to mitigate the grid crossing error. The Convected Particle Domain Interpolation (CPDI) is one such implementation.This paper extends the two-dimensional CPDI formulation for an axisymmetric problem where a pile is driven into sand that is modelled as a hypoplastic model. The extended formulation is tested, validated and compared to that for the case of the two-dimensional plane-strain within the framework of the method of manufactured solution. The hammer blows on the pile are represented by a periodic forcing function. In contrast to earlier studies on pile installation using advanced models, deep penetration is achieved in the present analysis. A non-regular distribution for the particle domains is suggested to avoid unnecessary computation. A frictional contact algorithm is introduced to describe the pile–soil interaction. 相似文献
15.
工程活动与地质环境 总被引：2，自引：0，他引：2
文中阐述了工程活动与地质环境的依存关系，指出了地质工程和环境工程地质是工程地质发展的两个重要生长点。工程活动成功与否，不仅决定于人们对工程建设区的工程地质条件认识程度，而且更重要的是对工程建设区的地质环境质量的认识程度，提出了在进行工程地质条件勘察的同时，必须对建设区的地质环境质量进行评价和对工程竣工后运营期间在建成的工程活动作用下对地质环境改变趋势及结果进行预测，为预防地质环境恶化和塑造人地和谐的地质环境提供依据。为了认识地质环境特征和进行环境工程地质预测，作者提出了地质环境模型概念。根据地质结构及地质体赋存环境因素(活动断层、地应力、地下水、地表水等)，以及作者的实践经验，在文中概括列举了５大类１９种不良地质环境模型典型实例。 相似文献
16.
数值天气预报检验方法研究进展 总被引：10，自引：1，他引：9
数值天气预报检验是改进及应用数值模式的重要环节。近年来,模式检验中的观念不断更新,适用于不同预报产品及不同用户需求的模式检验方法也不断涌现。首先简单回顾了以列联表为基础的传统的模式检验方法。其次重点总结了伴随高分辨率数值预报而出现的空间诊断检验技术,按照检验目的的不同,诊断方法可以归纳为:①基于滤波技术的分辨模式在不同时空尺度上预报能力的邻域法、尺度分离法;②利用位移偏差诊断模式预报位置、面积、方位、轴角等与观测差异的属性判别法、变形评估法。然后阐述了集合样本成员的概率分布函数(PDF)、集合预报与观测概率分布函数相似程度、事件发生的概率预报等集合预报检验方法。最后论述了空间诊断技术、集合预报检验方法的适用领域,并讨论了模式检验中存在的一些问题及未来的发展方向。 相似文献
17.
Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9–10???1; for the SVM model, a radial basis function was selected as the kernel function with best C?=?1 and γ = 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight- of- evidence (W- of- E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area. 相似文献
18.
The numerical modeling of unsaturated soil processes is becoming more prevalent worldwide. Although numerical modeling is
becoming increasingly accepted in geotechnical engineering practice, care must be exercised and improper modeling techniques
and procedures must be avoided. Many issues such as nodal resolution and imperfect convergence can result in inaccurate solutions.
Unfortunately, analyses of highly nonlinear unsaturated soil flow and slope stability models can significantly increase the
modeling time required. As a result, there is a trend to reduce the number of model runs. Results are often presented to client
as single model runs or simplistic sensitivity analysis. This paper presents methodologies for applying probabilistic methods
to unsaturated soils seepage and slope stability analysis models. The focus is on the application of the alternative point
estimate method to practical problems in such a way as to minimize the number of model runs. The demonstration of a successful
application to a waste rock pile is presented. 相似文献
19.
目前对堆积层滑坡的变形预测大多基于数学模型或方法,忽略了引起滑坡位移显著变化的动力外因及滑坡自身的地质特征,因此,预报准确度和可信度较低。以三峡库区典型堆积层滑坡--鹤峰场镇滑坡为例,通过4组主要控制因素科学组合构建了滑坡的基本地质模型;以此为基础,重点考虑引起滑坡发生变形的库水作用动力因素,建立滑坡的数值-力学模型。通过实际监测点的变形监测结果与数值-力学模型中模型监测点的变形进行拟合分析,获取了实际时间与数值-力学模型中时步的等效关系;基于时间-时步等效关系及三峡水库设计水位调度曲线,得到了不同时步水位的波动特征;通过时步的外延,并在相应的时步段对数值-力学模型施加等效时间的库水作用,预测了滑坡在未来库水位变动条件下的变形。该预测方法既考虑了滑坡的工程地质模型又考虑了地下水作用效应,克服了纯数学方法预测的不足。 相似文献