上海市地面沉降泊松旋回长期预测

本文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定的改进,建立了地面沉降泊松旋回预测的数学模型,提出了应用于预测的技术处理方法,适宜于进行地面沉降长期预测。预测的上海部分标点１９９５￣２０５０年地面沉降量１０￣３７ｃｍ,并对这一预测成果进行了一定评估,认为,在现有研究水平上是可信的。把衰减系数引入泊松旋回模型中是本方法的特色之一。     

西安地面沉降及地裂缝阶段预测

地面沉降及地裂缝可视其为复合地质灾害现象。二者性质不同,但就其发展阶段或单旋回过程而言,皆可将其归并于有限系统。本文讨论了西安市小寨地面沉降带的地质背景,建立了泊松旋回预测方程,论证了二者的演化规律及其盛衰阶段。引入指数函数法,进行了沉降阶段的对比研究。根据历史记录,讨论了西安地裂缝兴衰与我国陆块板内地震活动的对应关系。     

常州市地面沉降灰色模型预测

地面沉降过程可视为有限体系,本文分析了常州市地面沉降动态特征,采用费尔哈斯特(Verhulst)生物繁殖模型,以监测资料为背景予以灰色系统理论处理和进行沉降旋回期或寿命预测。在地下水得到控制开采和回灌补给条件下,常州市地面沉降旋回期为60年,至2030年沉降速率超近于零,为常州市合理开发地下水,控制沉降灾害提供宏观中长期预测信息。     

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。     

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。     

太原市地面沉降的计算与预测

为分析太原市地面沉降,对太原市区进行了地质模型概化,建立了太原市区大规模开采地下水引起地面沉降的数值计算模型,同时对研究区内的地面沉降进行了预测。分析结果表明,太原市地面沉降主要是由于集中开采深层承压含水层引起的。      

地铁施工中地面沉降预测研究

本文对地铁隧道施工中地面沉降模型进行了研究,并且应用这些模型,结合数值分析软件,对具体工程的地面沉降进行了预测。预测结果对于后续施工工艺的选择起到了指导性的作用,取得了良好的经济效益。     

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。     

阜阳市地面沉降趋势预测

阜阳市地面沉降是典型的因抽汲松散岩层地下水引起水位(压)下降而造成的地面沉降。本文采用固结理论、回归分析、灰色理论等三种方法建立地面沉降预测模型,旨在探讨在未建基岩标、分层标的城市如何定量研究地面沉降,同时对该市地面沉降的发展趋势进行预测,提出控制地面沉降的措施。     

常州地面沉降的灰色Verhulst预测

文章首先指出了土体的固结沉降过程具有“S”型曲线特征,这与Verhulst模型（Logistic生长模型）具有相似性.然后比较系统地给出了Logistic生长模型、模型的参数估计、模型的精度检验及适用条件,并将模型用于常州市清凉小学地面沉降过程的模拟,进行了初步预测.研究表明,灰色Verhulst模型在预测地面沉降方面是十分有效的,特别是发生在主采层及顶、底板土层中的变形,该模型给出地面沉降预测值时间序列与实测值序列是相当接近的.     

Application of grey theory-based model to prediction of land subsidence due to engineering environment in Shanghai

Land subsidence is a common geological hazard. The long-term accumulation of land subsidence in Shanghai has caused economic loss to the city. Since the 1990s, the engineering structures have become a new cause of land subsidence. Many factors affect the process of land subsidence. Although such a process cannot be explicitly expressed by a mathematical formula, it is not a “black box” whose internal structure, parameters, and characteristics are unknown. Therefore, the grey theory can be applied to the prediction of land subsidence and provides useful information for the control of land subsidence. In this paper, a grey model (GM) GM (1, 1) with unequal time-intervals was used to predict the subsidence of a high-rise building in the Lujiazui area of Shanghai, and the results were compared with the monitored data. The prediction of subsidence was also corroborated by laboratory tests and the results were compared with measured data and the predicted data by the adaptive neuro-fuzzy inference system (ANFIS). It is found that the GM (1, 1) with unequal time-intervals is accurate and feasible for the prediction of land subsidence.     

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.     

2001-2020年上海市地面沉降灾害经济损失评估

地面沉降是加速潮灾、涝灾等自然灾害的风险源.由于上海市市政建设和高层建筑的建设以及周边地区继续抽取地下水的影响,地面沉降趋势仍在继续.这使上海市在未来必将遭受地面沉降灾害所产生的巨大经济损失.分析了上海市未来地面沉降灾害产生经济损失的可能性、损失程度等.通过对影响未来地面沉降灾害经济损失不确定因素的分析,运用统计方法评估了2001-2020年间上海市地面沉降灾害风险的经济损失.经评估,2001-2020年上海区地面沉降灾害风险经济损失总额为245.7亿元.     

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.     

基坑施工引发的工程性地面沉降研究

通过对基坑施工引发的工程性地面沉降进行影响因素分析,结合地质结构条件,划分了上海地区典型基坑模型,采用数值方法分析了不同基坑模型在不同围护结构变形模式和降水条件下引发地面沉降的基本规律,提出了不同类型基坑工程引发的地面沉降影响范围,并在基坑工程实例中得到了有效验证.     

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.     

Groundwater resources management under environmental constraints in Shiroishi of Saga plain, Japan

A sinking of the land surface due to the pumping of groundwater has long been recognized as an environmental issue in the Shiroishi plain of Saga, Japan. Land subsidence can have several negative economic and social implications such as changes in groundwater and surface water flow patterns, restrictions on pumping in land subsidence prone areas, localized flooding, failure of well casings as well as shearing of structures. To minimize such an environmental effect, groundwater management should be considered in this area. In this study, a new integrated numerical model that integrates a three-dimensional numerical groundwater flow model coupled with a one-dimensional soil consolidation model and a groundwater optimization model was developed to simulate groundwater movement, to predict ground settlement and to search for optimal safe yield of groundwater without violating physical, environmental and socio-economic constraints. It is found that groundwater levels in the aquifers greatly vary from season to season in response to the varying climatic and pumping conditions. Consequently, land subsidence has occurred rapidly throughout the area with the Shiroishi plain being the most prone. The predicted optimal safe yield of the pumping amount is about 5 million m³. The study also suggests that pumping with this optimal amount will minimize the rate of land subsidence over the entire area. An erratum to this article can be found at     

Monitoring land subsidence in Semarang,Indonesia

Semarang is one of the biggest cities in Indonesia and nowadays suffering from extended land subsidence, which is due to groundwater withdrawal, to natural consolidation of alluvium soil and to the load of constructions. Land subsidence causes damages to infrastructure, buildings, and results in tides moving into low-lying areas. Up to the present, there has been no comprehensive information about the land subsidence and its monitoring in Semarang. This paper examines digital elevation model (DEM) and benchmark data in Geographic Information System (GIS) raster operation for the monitoring of the land subsidence in Semarang. This method will predict and quantify the extent of subsidence in future years. The future land subsidence prediction is generated from the expected future DEM in GIS environment using ILWIS package. The procedure is useful especially in areas with scarce data. The resulting maps designate the area of land subsidence that increases rapidly and it is predicted that in 2020, an area of 27.5 ha will be situated 1.5–2.0 m below sea level. This calculation is based on the assumption that the rate of land subsidence is linear and no action is taken to protect the area from subsidence.