工程环境效应引起上海地面沉降预测

地面沉降是目前常见的地质灾害之一,其长期的积累给城市带来巨大的经济损失,成为制约城市发展的主要因素。进入20世纪90年代以来,工程环境效应诱发的沉降已经成为上海地面沉降的新趋势,对于外荷载引起的地面沉降过程而言,影响因素较多,既无法用明确的数学关系式表达,又非黑箱那样内部结构、参数和特征一无所知,因此可将灰色预测理论应用于地面沉降的预测。针对监测和观测时间的非等时性,本文应用非等时距灰色理论模型对上海陆家嘴地区某高层建筑的沉降进行预测,并和实际监测沉降量进行了比较;对室内模型试验进行沉降预测,并和实验观测数据以及自适应神经网络系统(ANFIS)预测结果进行了比较。研究发现,对于工程环境效应引起的地面沉降,应用非等时距灰色理论模型进行沉降预测是可行、精确的。     

上海市地面沉降动态分析与灰色预测

本文应用灰色系统理论的关联度分析法,分析了上海市地面沉降在时间、空间上的变化特征,进而以市区和近郊区典型地段的地面沉降监测资料为基础,建立了灰色预测模型,并在某些假定前提下预测了上海市地面最终沉降量及“沉降寿命”。     

参数累积估计灰色模型及地面沉降预测

基于传统GM（1,1）地面沉降预测模型的非稳定性,引入参数累积估计方法来代替最小二乘法,构建了参数累积估计的灰色沉降预测模型。通过对上海市地面沉降的预测,证实模型降低了矩阵条件数,提高了沉降预测的稳定性,进而对上海市分层沉降进行了预测,并给出相应的模型稳定性判别,预测结果给出了上海沉降的发展趋势,为地面沉降的合理防治提供了帮助。     

基于灰色支持向量机残差修正模型的地面沉降量预测

沉降现象在各地区普遍发生,地面沉降量预测越来越受到重视。本文通过结合灰色(GM(1,1))预测模型和支持向量机(SVM)模型各自的优点,建立灰色支持向量机(GM(1,1)-SVM)残差修正模型,在突出时间序列发展趋势影响的同时降低序列中异常值的消极作用。以某高层建筑的18次地面沉降量数据为实例,检验GM(1,1)-SVM模型的预测效果。结果表明:相对单一的GM(1,1)沉降量预测模型,GM(1,1)-SVM模型相对误差小,预测精度高,对地面沉降量预测有一定指导意义。     

New grey prediction model and its application in forecasting land subsidence in coal mine

Mining subsidence destroys environment seriously and is difficult to forecast because the parameters in prediction model are difficult to obtain. As there are many uncertainties in mining subsidence, we forecast it by grey prediction model. Traditional GM (1,1) model predict for a time series. In this paper, the panel data are studied and are viewed as a sequence in which elements are matrix based on cross-sectional data, and the mean sequence of row vector GM (1,1) model, mean sequence of column vector GM (1,1) model and the cell volume sequence GM (1,1) model are established, respectively. Combining these grey models, we build prediction model of cross-sectional data matrix sequence. Thus, the scope of grey prediction has been expanded, and grey forecasting theory has been enriched. Using the newly built predictive models, we study the land deformation due to mining of Pingdingshan coal mine in Henan Province. Practical verification and model accuracy test show that the grey model can make accurate predictions, with a good agreement between the predictive value and actual value. It can provide effective and accurate information and also can provide an important reference for the reclamation planning of surface environment.     

灰色系统在地面沉降分析中的应用

该文将灰色系统理论引入地面沉降的研究之中。根据上海地面沉降的历史数据建立了上海地面沉降发展的GM(1,1)模型以及地面沉降与地下水位变化的映射GM(1,2)模型,最后运用所建立的模型对地面沉降的发展态势进行了预测。     

软土地区工程性地面沉降预测的非等时距GM(1,1)模型

大规模城市建设诱发的工程性地面沉降已成为上海软土地区地面沉降新的制约因素。本文针对上海地区工程性地面沉降特点,运用灰色理论建立了非等时距GM(1,1)预测模型,并用工程实测数据进行检验,得到令人满意的预测结果。研究结果对预测和控制软土地区由建筑物群引起的地面沉降具有重要意义。     

地面沉降模拟计算的灰色离散GM（1，1）模型

地面沉降的模拟计算属于灰色问题,建立一个有效的灰色预测模型是十分重要的,在分析灰色线性回归组合模型模拟序列特点的基础上,建立了以原始数据直接建模的离散GM（1,1）模型（称为ODGM（1,1）模型）,将某沉降实例数据建立ODGM（1,1）模型,并与灰色线性回归组合预测模型进行比较,结果证明离散GM（1,1）模型优于灰色线性回归组合预测模型。     

地面沉降泊松旋回预测方法：以上海地面沉降长期预测为例

在上海地面沉降长期预测研究的实际经验基础上,对泊松旋回模型进行了一些改进,建立了地面沉降泊松旋回预测的数学模型,提出了应用于预测时的技术处理方法。对预测的上海部分标点1995—2050年地面沉降量10～37cm进行了一定评估,认为在现有研究水平上是可信的。引入衰减系数于泊松旋回模型之中是本方法的特色之一。     

天津市地面沉降的灰色系统—马尔柯夫预测模型应用

地面沉降速率监测值多呈波形动态,数据波动给预测带来了困难。本文以天津地面沉降多个水准点的监测数据为基本序列,介绍了传统灰色系统模型GM（1,1）的预测过程及结果,并在此基础上进行马尔柯夫处理,建立灰色系统-马尔柯夫预测模型。其结果表明,后者预测精度明显提高,67％的样本误差可降至10％以内,92％的样本预测值误差低于30％。     

The state of land subsidence and prediction approaches due to groundwater withdrawal in China

This article gives a general introduction to land subsidence with the prediction approaches due to withdrawal of groundwater in three subsided/subsiding regions in China: the deltaic plain of Yangtse River (YRDP), North China Plain (NCP), and Fenwei Plain (FP). On YRDP, Shanghai is the typical subsided/subsiding city; on NCP Tianjin is the typical subsided/subsiding city, and on FP Taiyuan is the typical subsided/subsiding city. The subsided area with subsidence over 200 mm on YRDP is about 10,000 km² and the maximum subsided value reached 2.9 m at Shanghai; on NCP the subsided area reached 60,000 km² with the maximum subsidence of 3.9 m at Tianjing; on FP the subsided area is relatively smaller than that on the other two plains and is about 1,135 km² with maximum subsidence of 3.7 m at Taiyuan city. In order to protect the civil and industrial facilities, it is necessary to predict the future development of land subsidence based on present state. Many researchers proposed several approaches to predict the land subsidence due to groundwater withdrawal according to different geological conditions and groundwater withdrawal practice. This article classifies these approaches into five categories: (i) statistical methods; (ii) 1D numerical method; (iii) quasi-3D seepage model; (iv) 3D seepage model; (v) fully coupled 3D model. In China, the former four categories are presently employed in the prediction practice and their merits and demerits are discussed. According to the prediction practice, 3D seepage model is the best method presently.     

Application of geo-environmental capacity of ground buildings in urban planning

The restrictions of the geo-environment are often ignored in urban planning, thereby directly causing a variety of geological hazards, including large areas of land subsidence in soft soil area. Based on the control objective of land subsidence, the geo-environmental capacity of ground buildings (GECGB) is defined. The relationship between floor area ratio (FAR) and land subsidence in Shanghai, China is analyzed. The results illustrate that land subsidence increases as FAR increases, and that the engineering environmental effect of the high-rise building group is the main factor affecting land subsidence in Shanghai, China. Hayashi’s Quantification Theory type I is selected to evaluate the GECGB of four typical areas in Shanghai. The prediction model is established based on existing background materials and the GECGB expressed by allowable FAR of the four typical areas are calculated. The evaluation approach promoted in this paper can be applied in urban planning to control the land subsidence induced by dense high-rise buildings.     

边坡变形的灰色预测模型

利用灰色系统理论建立边坡变形观测资料的 G M( 1 , 1) 模型群, 在建模中考虑了观测资料的非等间隔性, 模型较好地反映了边坡变形的趋势。     

地面沉降预测的灰色-马尔柯夫模型

天津滨海新区地处京津和环渤海两大城市带的交汇点,与日本、韩国隔海相望,是我国对外开放的重要通道。滨海新区广泛分布有软弱土层,属于欠固结地层,不仅会自然压密,而且在荷载的长期作用下容易产生次固结变形;长期以来,由于地表水资源短缺,大幅超采地下水的形势严峻。自然因素与人为活动的综合作用,使得滨海新区地面沉降现象普遍发生,形势不容忽视。应用一定的理论与方法,预测地面沉降可能的发展趋势,以寻求有效的防治措施,是天津滨海新区地面沉降研究的最紧迫任务之一。地面沉降是一种渐变性地质灾害,可以预测其发展趋势。文章结合灰色系统理论预测模型及马尔柯夫预测模型特点,提出了地面沉降预测的灰色-马尔柯夫模型,并应用实例演示灰色-马尔柯夫模型预测过程。为便于大量监测点的预测,采用C＋＋编程对天津滨海新区170个监测点进行预测,结果证明灰色-马尔柯夫模型对天津滨海新区地面沉降趋势预测具有实际意义,表明此模型对随机性强,波动性大的时间数列预测具有较好的精度。在实际应用中,灰色-马尔柯夫模型一般需要较多的原始数据,原始数据越多,预测精度越高。     

Modeling visco-elastic–plastic deformation of soil with modified Merchant model

Eighty-four to ninety-eight percent of land subsidence in Shanghai City is caused by the visco-elastic–plastic deformation of sediments. Numerical experiments are done on the sediments with visco-elastic–plastic deformation in Shanghai to verify the modified Merchant model (MM model) and land subsidence model based on the modified Merchant (LS-MM) model. There are two advantages of the MM model and the LS-MM model. One is that only a few parameters are involved. There are three parameters in the MM model and four parameters in the LS-MM model. The other one is that both models can describe elastic, elastic–plastic and visco-elastic deformation in addition to visco-elastic–plastic deformation. The corresponding models are developed by setting proper values of the three parameters of μ, α ₁ and α ₂. The two advantages make the LS-MM model flexible and applicable to the simulation of the large regional land subsidence with visco-elastic–plastic deformation and other different kinds of deformation. The results can be improved by variable parameters, especially specific storage.     

基于灰色GM（1，1）模型的城市生活用水量预测

介绍灰色理论建模原理和模型参数辨识方法,并以上海市为实例建立灰色GM（1,1）预测模型,运用残差检验、后验差检验以及关联度检验3种方法对模型进行精度检验,其模型拟合精度达97．25％。用所建立的模型对上海市2003～2007年城市生活用水量进行预测。结果表明,该灰色模型用于城市生活用水量预测,符合其灰色特性,通用性好,并且所需数据少,计算量适中,预测结果与实际情况比较吻合。     

The microscopic characteristics of Shanghai soft clay and its effect on soil body deformation and land subsidence

Compaction of the 40-m-thick, upper Shanghai soft clay contributes to land subsidence in the Shanghai region. The upper clay, a marine deposit, continuously subsides at rate of 3 mm/years despite mitigation strategies designed to control land subsidence. These strategies include the artificial injection of water into the subsurface. Data describing the particle-size distribution, pore-size distribution, microstructure, mineralogical composition, pore-solution composition, and cation-exchange capacity indicate that the clay is semidispersed, substable, and susceptible to compaction. An evaluation of soil body deformation caused by artificial injection of dilute water into a marine clay suggests that depression of the electrostatic double 1ayer in the marine clays may be responsible for the observed compaction and resulting subsidence. A decrease in the swelling pressure of the marine clays might result from a reduction in the concentration of dissolved sodium in pore fluids when calcium-bicarbonate water is injected. Thus, ironically, the strategy designed to mitigate land subsidence could be contributing to it. Increasing the concentration of dissolved salts in the injection water may prevent the collapse of the clay structure and halt or reverse the subsidence process. The analysis results indicate the deformation of soft clay by consolidation is subject to its microscopic physical and chemical features, rather than the fluctuation of groundwater level. This continuous compression contributes as a major part to the land subsidence in Shanghai. Supported by Programmer of Excellent Young Scientists of the Ministry of Land and Resources People’s Republic of China, No.2002-18.     

Three-dimensional numerical modeling of land subsidence in Shanghai,China

Shanghai, in China, has experienced two periods of rapid land subsidence mainly caused by groundwater exploitation related to economic and population growth. The first period occurred during 1956–1965 and was characterized by an average land subsidence rate of 83 mm/yr, and the second period occurred during 1990–1998 with an average subsidence rate of 16 mm/yr. Owing to the establishment of monitoring networks for groundwater levels and land subsidence, a valuable dataset has been collected since the 1960s and used to develop regional land subsidence models applied to manage groundwater resources and mitigate land subsidence. The previous geomechanical modeling approaches to simulate land subsidence were based on one-dimensional (1D) vertical stress and deformation. In this study, a numerical model of land subsidence is developed to simulate explicitly coupled three-dimensional (3D) groundwater flow and 3D aquifer-system displacements in downtown Shanghai from 30 December 1979 to 30 December 1995. The model is calibrated using piezometric, geodetic-leveling, and borehole extensometer measurements made during the 16-year simulation period. The 3D model satisfactorily reproduces the measured piezometric and deformation observations. For the first time, the capability exists to provide some preliminary estimations on the horizontal displacement field associated with the well-known land subsidence in Shanghai and for which no measurements are available. The simulated horizontal displacements peak at 11 mm, i.e. less than 10 % of the simulated maximum land subsidence, and seems too small to seriously damage infrastructure such as the subways (metro lines) in the center area of Shanghai.     

Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.     

改进欧拉算法的GM(1,1)模型预测滑坡变形

在传统的GM(1,1)模型建模的基础上,采用改进欧拉算法简化GM(1,1)模型中参数的求解过程,进行滑坡变形动态预测。本文根据某高速公路滑坡治理过程中的变形监测数据,应用改进的模型进行滑坡变形动态预测,通过预测结果和实际监测数据进行对比分析,结果表明:改进欧拉算法的GM(1,1)模型参数计算简单,且预测精度为一级;不仅适用于滑坡变形等时距监测数据的低增长序列预测,也适用于高增长序列预测,证明该改进欧拉算法的有效性。