紫阳滑坡治理工程实例分析

本文介绍紫阳骨坡治理工程中有关抗滑桩的设计与施工。文中详细地论述了设计中设计人员应根据工程的具体情况去灵活处理的一些技术问题，如确定有关参数的选择原则，计算上的条件简化和一些具体技术细节的处理方法等．     

企鹅珍珠贝人工苗生长的初步观察

为了解企鹅珍珠贝生长规律，观察了壳高2.5～5.0mm的出池幼苗，按月测量其生长参数和成活率，以及环境因子。结果表明，企鹅珍珠贝生长最快的月份为7～11月和次年4～6月，壳长，壳高和壳宽月均增加量分别为3.8～13.0mm、4.7～11.2mm、2.3～3.8mm，月成活率97.8%～98.6%。企鹅珍珠贝生长最慢月份是11月至次年3月。     

Reliability assessment of serviceability performance of braced retaining walls using a neural network approach

In urban environments, one major concern with deep excavations in soft clay is the potentially large ground deformations in and around the excavation. Excessive movements can damage adjacent buildings and utilities. There are many uncertainties associated with the calculation of the ultimate or serviceability performance of a braced excavation system. These include the variabilities of the loadings, geotechnical soil properties, and engineering and geometrical properties of the wall. A risk‐based approach to serviceability performance failure is necessary to incorporate systematically the uncertainties associated with the various design parameters. This paper demonstrates the use of an integrated neural network–reliability method to assess the risk of serviceability failure through the calculation of the reliability index. By first performing a series of parametric studies using the finite element method and then approximating the non‐linear limit state surface (the boundary separating the safe and ‘failure’ domains) through a neural network model, the reliability index can be determined with the aid of a spreadsheet. Two illustrative examples are presented to show how the serviceability performance for braced excavation problems can be assessed using the reliability index. Copyright © 2005 John Wiley & Sons, Ltd.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

桩前预留土体对抗滑桩影响的分析与计算研究

本文在前人研究的基础上对桩前土体需要开挖的抗滑桩提出了一种新的计算方法,即似m法。文中将单排抗滑桩分为三类:第一类为桩前土体不开挖,但与抗滑桩分离形成桩后缝隙;第二类为桩前土体不开挖,但与抗滑桩相贴且有抵挡抗滑桩的作用;第三类为桩前土体需要开挖,并对各类抗滑桩给出了相应的设计计算方法。对于第一类抗滑桩可用本文改进的m法和有限线单元法计算;对于第二类抗滑桩考虑桩前抗力值大小后也可用本文改进的m法和有限单元法计算;对于第三类抗滑桩可用本文提出的似m法和改进的有限线单元法计算。通过与实际工程测量值对比分析,得出结论:在抗滑桩工程中对桩的钢筋受力进行实测以推断桩的受力性能,弥补滑坡推力及桩前抗力勘测、分析、计算值可靠性差的弱点。该方法与二维、三维有限元法相比,具有计算方法简单、可靠的特点,值得广泛应用和发展。     

基于Poulos弹性理论的被动桩改进算法

传统的Poulos弹性理论仅适合于均质土中土体侧向位移时桩的性状分析,无法考虑土的层状特性。通过引入层状地基中作用一水平集中力的广义Mindlin解和地面作用有竖向荷载时的应力和位移通解,对Poulos方法进行了改进,使之扩展到多层土中,还用于研究堆载条件下的被动桩变形和受力响应。算例分析表明,改进弹性理论要比Poulos方法更为严密、合理,提高了计算精度,应用范围也更广。     

高桩码头桩基负摩擦力现场试验研究 ——上海洋山深水港工程现场试验

高桩码头接岸结构中的桩基础,在抛石的作用下将产生负摩擦力。通过支撑桩及自由桩上负摩擦力的现场试验研究,获得抛石过程中基桩内力随深度变化的基本规律。抛石过程中基桩上的负摩擦力主要产生在抛石段。同时,抛石作为附加荷载,将引起下方软弱土层的固结沉降,桩土之间产生相对位移,从而在抛石段下方一定范围内也产生负摩擦力作用。承台荷载对负摩擦力的大小和分布有着重要的影响。提出的负摩擦力分布规律和采用有效应力法计算层状土负摩擦力的公式,可为桩基础的设计,尤其对水下单面抛石施工的设计及采取消减负摩擦力的措施提供依据。     

Efficiency analysis of open trench for impact pile driving through a single-variable method

Abstract

The ground vibrations during pile driving operation have a drastic potential to undermine the surrounding structures both in land and reclaimed land. Particularly, reclaimed land necessitates ample application of pile driving due to the weak land condition. To prevent the structural damage, attenuation of the ground vibrations to an allowable level through active isolation of circular open trench is the scope of this study. In this research, finite element simulations of continuous impact pile driving process from the ground surface was executed with particular attention to the pile-soil interaction, and thereby, the efficiency of open trench application in attenuation of the unsafe distance of different structures was surveyed using the vibration sensitivity degree. Regarding the crucial parameters of an open trench (depth, width, and location), it was concluded that a sufficient high depth can attenuate the unsafe distance up to 68%, the trench width variations are less effective, and an average pile-trench distance is the most efficient option. The excavation volume was also concluded as another crucial parameter in open trench design which takes all three parameters into account. The trench depth equal to the pile’s maximum critical depth of vibration was inferred for an optimum design.