推求无资料地区设计洪水的一种方法

在地貌单位线、菲利浦下渗公式和暴雨特征频率分布的基础上,引用产流开始时刻和净雨量的概念,推求不同净雨情况下不产流暴雨事件的概率,从而求得洪峰流量小于和等于给定Q_p值的理论频率分布及其相应的重现期。在贵州和山西应用结果表明,该方法更适用于半干旱地区。由模型验证实例分析了这种理论洪水频率分布模型的应用前景。     

Geotechnical properties and behaviour of Nigerian tar sand

A brief review of the geology of the tar sand areas of Nigeria is given. Analysis shows that the tar sand used for the tests consists of a well graded silty sand (cmf) with about 5% clay; and 3–5% bitumen. Results presented show a very high in situ compressive strength of about 450 kN/m², a high ratio of tensile to compressive strength of about 22%, peak shear strength parameters of C_p′=15kN/m²and φ_p′ = 19° and residual parameters of C_r =0, φ_r = 18°. The compacted tar sand behaved like an overconsolidated soil with a preconsolidating pressure, P_c of 140 kN/m2. In general, the results of the in situ strength tests indicate that the Nigerian tar sand behaved as a soft sandstone.     

An intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors

Biofiltration has shown to be a promising technique for handling malodours arising from process industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady state and shock loaded conditions were modelled using the basic principles of artificial neural networks. Artificial neural networks are powerful data driven modelling tools which has the potential to approximate and interpret complex input/output relationships based on the given sets of data matrix. A predictive computerised approach has been proposed to predict the performance parameters namely, removal efficiency and elimination capacity using inlet concentration, loading rate, flow rate and pressure drop as the input parameters to the artificial neural network model. Earlier, experiments from continuous operation in the biofilter showed removal efficiencies from 50 to 100 % at inlet loading rates varying up to 13 g H₂S/m³h. The internal network parameter of the artificial neural network model during simulation was selected using the 2^k factorial design and the best network topology for the model was thus estimated. The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter performance effectively with R² values of 0.9157 and 0.9965 for removal efficiency and elimination capacity in the test data. The proposed artificial neural network model for biofilter operation could be used as a potential alternative for knowledge based models through proper training and testing of the state variables.     

Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks

The main objective in production blasting is to achieve a proper fragmentation. In this paper, rock fragmentation the Sarcheshmeh copper mine has been predicted by developing a model using artificial neural network. To construct the model, parameters such as burden to spacing ratio, hole-diameter, stemming, total charge-per-delay and point load index have been considered as input parameters. A model with architecture 9-8-5-1 trained by back propagation method was found to be optimum. To compare performance of the neural network, statistical method was also applied. Determination coefficient (R ²) and root mean square error were calculated for both the models, which show absolute superiority of neural network over traditional statistical method.     

Ultimate Bearing Capacity Prediction of Eccentrically Inclined Loaded Strip Footings

Extensive laboratory model tests have been carried out on a strip footing resting over dry sand bed subjected to eccentrically inclined load to determine the ultimate bearing capacity (Patra et al. in Int J Geotech Eng 6(3):343–352, 2012a.  https://doi.org/10.3328/IJGE.2012.06.03.343-352, Int J Geotech Eng 6(4):507–514, b.  https://doi.org/10.3328/IJGE.2012.06.04.507-514). Similarly, lower bound calculations based on finite element method were performed to compute the bearing capacity of a strip footing subjected to an eccentric and inclined load lying over a cohesionless soil with varying embedment depth and relative density (Krabbenhoft et al. in Int J Geomech ASCE, 2014.  https://doi.org/10.1061/(ASCE)GM.1943-5622.0000332). The load may be applied in two ways namely, towards the center line and away from the center line of the footing. Based on the results (both experimental and numerical analyses), a neural network model is developed to predict the reduction factor that will be used in computing the ultimate bearing capacity of an eccentrically inclined loaded strip footing. This reduction factor (RF) is the ratio of the ultimate bearing capacity of the footing subjected to an eccentrically inclined load to the ultimate bearing capacity of the footing subjected to a centric vertical load. A thorough sensitivity analysis is carried out to evaluate the parameters affecting the reduction factor. Based on the weights of the developed neural network model, a neural interpretation diagram is developed to find out whether the input parameters have direct or inverse effect on the output. A prediction model equation is framed with the trained weights of the neural network as the model parameters. The predictions from ANN, and those from other approaches, are compared with the results computed from both experimentation and FEM analyses. The ANN model results are found to be more accurate and well matched with other results.     

Prediction of pile capacity from stress-wave measurements: A neural network approach

A neural network approach for the prediction of pile bearing capacity by the stress-wave matching technique is presented. The main advantage of this approach over the traditional manual or automated matching approach is that it avoids the time-consuming process of iterative adjustment. This makes it feasible to determine the static pile capacity in real time in the field. Another benefit of this approach is that as more case histories become available, the neural network can be improved by learning from these new examples. A three-layer back-propagation network is set up to illustrate the capability of the proposed approach for 70 dynamically tested concrete bored piles. A wave equation model developed at the National University of Singapore and coded in the NUSWAP computer program is used to formulate the problem. Up to 14 of the 70 piles (20 percent) are used in training the network. The NUSWAP program is used to generate simulation training examples based on the manually fitted training examples for further training of the network. Different network configurations are examined. The trained network produces results exhibiting good stress-wave matching qualities compared to those obtained by manual fitting. The pile bearing capacities predicted by the two approaches agree very closely. The load-settlement curve and axial load distribution in the pile computed using the network-predicted soil parameters are in good agreement with the field measurements obtained from a maintained load test.     

CFD-DEM耦合模拟中拖曳力模型精度

CFD-DEM耦合方法已广泛应用于岩土流固耦合问题分析,其模拟准确性与其中用于处理颗粒-流体相互作用的拖曳力模型密切相关。为了探究拖曳力模型精度的影响因素,采用CFD-DEM耦合方法建立了水中单颗粒沉降数值模型,模拟中考虑了3种典型拖曳力模型以及多种颗粒尺寸,将模拟得到的最终沉降速度与经验公式预测结果进行对比,分析了不同颗粒雷诺数（Re_p）时3种拖曳力模型（Ergun、Wen和Yu模型,Di Felice模型,Hill和Koch模型）的模拟精度。结果表明,Ergun、Wen和Yu模型以及Di Felice模型的精度均随着Re_p的增大而降低,而Hill和Koch模型的精度随着Re_p的增大出现先升高后降低的趋势：一般情况下,当Re_p≤14以及Re_p>72时,3种拖曳力模型的精度从高到低顺序为Ergun、Wen和Yu模型> Di Felice模型> Hill和Koch模型;而当14< Re_p≤40时,Hill和Koch模型的精度最高,Di Felice模型的精度最低;当40<Re_p≤72时,3种拖曳力模型的精度从高到低顺序为Ergun、Wen和Yu模型>Hill和Koch模型>Di Felice模型。     

Prediction of Uplift Pile Displacement Based on Cone Penetration Tests (CPT)

Accurate prediction of uplift pile displacement is necessary to ensure appropriate structural and serviceability performance of civil projects. On the other hand, in recent years, machine-learning models have been applied to many geotechnical-engineering problems, with some degrees of success. The scope of this research includes three main stages: (1) the compilation of load–displacement data sets, obtained from the published literature, (2) analysis of machine learning models that predict the uplift pile displacement based on the cone penetration test data, and the relative importance of input parameters that have been evaluated using senility analysis by the artificial neural network, In addition, this paper also examines the different selection of input parameters and internal network parameters to obtain the optimum model, (3) A parametric study has also been performed for the input parameters to study the consistency of the suggested model. The statistical parameters and parametric study obtained in this research show the superiority of the current model. It is demonstrated that machine learning models such as ANN and GP models outperform the traditional methods, and provide accurate uplift pile displacement predictions.     

Regional time-predictable modeling in the Hindukush–Pamir–Himalayas region

The seismic characteristic of Hindukush–Pamir–Himalaya (HPH) and its vicinity is very peculiar and has experienced many widely distributed large earthquakes. Recent work on the time-dependent seismicity in the Hindukush–Pamir–Himalayas is mainly based on the so-called “regional time-predictable model”, which is expressed by the relation log T=cM_p+a, where T is the inter-event time between two successive main shocks of a region and M_p is the magnitude of the preceded main shock. Parameter a is a function of the magnitude of the minimum earthquake considered and of the tectonic loading and c is positive (0.3) constant. In 90% of the cases with sufficient data, parameter c was found to be positive, which strongly supports the validity of the model. In the present study, a different approach, which assumes no prior regionalization of the area, is attempted to check the validity of the model. Nine seismic sources were defined within the considered region and the inter-event time of strong shallow main shock were determined and used for each source in an attempt at long-term prediction, which show the clustering and occurrence of at least three earthquakes of magnitude 5.5≤M_s≤7.5 giving two repeat times, satisfying the necessary and sufficient conditions of time-predictable model (TP model). Further, using the global applicability of the regional time- and magnitude-predictable model, the following relations have been obtained: log T_t=0.19 M_min+0.52M_p+0.29 log m₀−10.63 and M_f=1.31M_min−0.60M_p−0.72 log m₀+21.01, where T_t is the inter-event time, measured in years; M_min the surface wave magnitude of the smallest main shock considered; M_p the magnitude of preceding main shock; M_f the magnitude of the following main shock; and m₀ the moment rate in each source per year.

These relations may be used for seismic hazard assessment in the region. Based on these relations and taking into account the time of occurrence and the magnitude of the last main shock in each seismogenic source, time-dependent conditional probabilities for the occurrence of the next large (M_s≥5.5) shallow main shocks during the next 20 years as well as the magnitudes of the expected main shocks are determined.     

河流断面形态问题

面积、平均水深相同的断面,形态不同,输水、输沙能力有较大差异。导得了断面特征值,包括表称流量、表称输沙率、表称含沙量、宽深比等的计算式。讨论了断面形态的变化及其表达方法。提出了几种平均水深的算法。讨论了常用的√B/H关系,认为用它来表达断面形态、判断变化性质是不合理的。     

Refinement of thrust faulting models for the Central New Madrid seismic zone

We present the results of the joint relocation of events recorded during 1989–1992 by the PANDA network in the central New Madrid seismic zone. The near-surface material in the study area is a gently-dipping layer of poorly consolidated sediments with low P-wave velocity and high V_p/V_s (estimated values: 1.8 km s⁻¹ and 3). The sediments are underlain by high-velocity Paleozoic rocks. Under the network the difference in sediment thickness is only 0.6 km, but because of the low velocities the location of the events using layered models is affected by errors. Application of the joint hypocentral determination (JHD) technique to a subset of 580 events shows that the single-event locations may be in error by as much as 1 km in depth, depending on where the events are located. Analysis of synthetic data generated for a realistic 3-D velocity model supports the JHD results. The analysis of synthetic data also suggests that a V_p/V_s≤ 2.3 is more appropriate for the post-Paleozoic Mississippi embayment sediments. Based on the JHD locations we present a new interpretation of the seismicity, with two en-echelon SW-dipping thrust faults connected by a west-dipping thrust fault. These faults appear associated with the Reelfoot scarp and its northern extension, the Kentucky bend scarp.     

Some applications of a backpropagation neural network in geo-engineering

A MATLAB based backpropagation neural network (BPNN) model has been developed. Two major geo-engineering applications, namely, earth slope movement and ground movement around tunnels, are identified. Data obtained from case studies are used to train and test the developed model and the ground movement is predicted with the help of input variables that have direct physical significance. A new approach is adopted by introducing an infiltration coefficient in the network architecture apart from antecedent rainfall, slope profile, groundwater level and strength parameters to predict the slope movement. The input variables for settlement around underground excavations are taken from literature. The neural network models demonstrate a promising result predicting fairly successfully the ground behavior in both cases. If input variables influencing output goals are clearly identified and if a decent number of quality data are available, backpropagation neural network can be successfully applied as mapping and prediction tools in geotechnical investigations.     

基于进化神经网络的参考作物腾发量预测

利用遗传算法的全局空间寻优功能和BP网络映射能力强的优点,建立了以遗传算法确定最优网络结构的进化神经网络(GA-ANN)模型,用来预测参考作物腾发量(ET₀).设计多组数字实验处理,研究了输入因子间相关性对模型预测准确性的影响,并验证了最优网络模型结构,即预测ET₀的理想GA-ANN模型中以日平均气温、日照时数及日序数为输入因子.实例分析表明,该模型克服了BP网络输入层、隐含层节点确定的盲目性,适应性强,精度高,可用于ET₀预测.     

基于ANFIS的单桩竖向极限承载力预测模型

人工神经网络已应用在岩土工程的各个方面。针对常用的BP网络的不足之处,建立了基于自适应神经模糊推理系统（ANFIS）的单桩竖向极限承载力预测模型。利用文献中桩的载荷试验数据来训练ANFIS网络,确定了网络参数。研究结果表明,同常用的BP网络相比,ANFIS预测模型具有学习速度快,拟合能力较好,训练结果唯一等优点,该方法是一种有效地预测单桩极限承载力的方法。     

Imaging the heterogeneous source area of the 2003 M6.4 northern Miyagi earthquake, NE Japan, by double-difference tomography

A shallow M6.4 inland earthquake occurred on 26 July 2003 in the northern part of Miyagi Prefecture, northeastern Japan. This earthquake was a typical inland thrust earthquake, a type that is common in NE Japan. We obtained a detailed seismic velocity structure in the focal area of this earthquake by the double-difference tomography method. Arrival-time data came from temporary seismic stations deployed above the mainshock fault plane. Both the P-wave and S-wave velocities in the hanging wall were lower than those in the footwall. Aftershocks were aligned along a zone where the seismic velocity changes rapidly. This is consistent with the interpretation that the 2003 northern Miyagi earthquake occurred along a fault that acted as a normal fault in the Miocene and has been reactivated as a reverse fault under the present compressional stress regime. The large slip area by the main shock rupture (asperity) corresponds to an area with relatively high P- and S-wave velocities. A zone with low V_p/V_s was detected along the aftershock area. One of the possible causes of this low-V_p/V_s zone is the existence of high-aspect-ratio pores that contain water. Hypocenters of the main shock, largest foreshock, and largest aftershock are also located within the low-V_p/V_s zone.     

Inclined load capacity of suction caissons

A simplified method of analysis for estimating lateral load capacity of suction caisson anchors based on an upper bound plasticity formulation is presented. The simplification restricts the analysis to caissons in uniform and linearly varying undrained strength profiles; nevertheless, its computational efficiency permits quick evaluation of a number of parameters affecting load capacity. The validity and limitations of the simplified formulation are demonstrated through comparisons to more rigorous finite element solutions. A series of sensitivity studies demonstrate the effects of various soil conditions and loading parameters. Copyright © 2003 John Wiley & Sons, Ltd.     

BP神经网络预测嵌岩桩承载力

确定嵌岩桩承载力的最可靠最直接的方法是静载试验,但是由于嵌岩桩承载力大,静载试验耗工费时,并且很难做到破坏,因此工程界希望能在不影响结果精度的前提下尽可能少做静载试验。利用以往的嵌岩桩静载试验资料,在BP神经网络理论的基础上,运用Matlab中的神经网络工具箱进行编程分析,总结出嵌岩桩的各种可控参数对其承载能力的影响,从而确定最终比较合理的嵌岩桩的设计参数。对比分析前人的研究成果,得出的结论具有一定的实用性。     

海上风电桩基水平承载力循环衰减二维简化分析

海上风电的桩基在长期循环荷载作用下会引起承载力的衰减。针对软黏土中水平受荷单桩,通过引入累积塑性应变以考虑土体不排水强度的循环弱化,建立二维有限元数值模拟和简化p-y曲线简化方法,以分析水平循环荷载作用后单桩桩侧侧向抗力的衰减弱化。在小数目循环荷载下简化方法与有限元计算结果比较吻合,在此基础上,采用二维简化分析方法得到长期大数目循环荷载下桩侧水平抗力的衰减规律,发现如荷载幅值与初始极限抗力的比值小于土体灵敏度的倒数,单桩在长期水平循环荷载作用下承载力虽有所衰减,但桩基趋于稳定,不会发生破坏。     

致密砂岩气层压裂产能及等级预测方法

致密砂岩储层孔隙度小、渗透率低、含气饱和度低,基本上没有自然产能,需要进行压裂,因此进行压裂产能的预测很有必要。笔者研究了鄂尔多斯盆地苏里格东部地区盒8段致密砂岩气层的压裂产能及等级预测。利用反映储层流动性质的测井参数(电阻率、自然伽马、声波时差、中子、密度)与反应压裂施工情况的压裂施工参数(单位厚度砂体积、砂比、砂质量浓度、单位厚度排量、单位厚度入井总液量),选择数学统计方法神经网络法进行致密砂岩气层压裂产能等级预测。分析比较Elman神经网络、支持向量回归(SVR)、广义回归神经网络(GRNN)3个神经网络预测致密砂岩气层压裂产能模型的网络结构参数、回判及预测精度以及运行耗费时间。结果表明:3个模型中,GRNN网络参数只有1个,回判和预测精度最高,运行时间小于1 s。因此,选择GRNN模型预测致密砂岩气层压裂产能,并用于苏里格东部地区致密砂岩气层压裂产能的等级预测。等级预测准确率达到90%。     

大庆深部致密砂砾岩含气储层产能预测

气层产能预测是气藏工程研究中用于指导气井以及气田合理生产的重要工作和任务,它在气田整体评价和高效开发进程中具有很强的预见性和主动性。讨论了大庆深部致密砂砾岩含气储层的产能与测井响应之间的关系,探讨了根据测井资料应用人工神经网络技术预测含气储层产能的方法。利用已知气井测试结果和测井资料作为网络的训练样本。根据网络学习训练结果,输入储集层的测井资料等静态参数,可预测该储集层的产能。根据这种关系采用神经网络技术实现了测井对产能的预测评价,从而为大庆深部致密砂砾岩含气储层的开发提供了一定的依据。