Excavation-induced ground movements and the resulting damages to adjacent structures and facilities is a source of concern for excavation projects in urban areas. The concern will be even higher if the adjacent structure is old or has low strength parameters like masonry building. Frame distortion and crack generation are predictors of building damage resulted from excavation-induced ground movements, which pose challenges to projects involving excavations. This study is aimed to investigate the relation between excavation-induced ground movements and damage probability of buildings in excavation affected distance. The main focus of this paper is on masonry buildings and excavations stabilized using soil nail wall method. To achieve this purpose, 21 masonry buildings adjacent to 12 excavation projects were studied. Parametric studies were performed by developing 3D FE models of brick walls and excavations stabilized using soil nail wall. Finally, probability evaluations were conducted to analyze the outputs obtained from case studies. Based on the obtained results, simple charts were established to estimate the damage of masonry structures in excavation affected distance with two key parameters including “Displacement Ratio” and “Normalized Distance”. The results also highlight the effects of building distance from excavation wall on its damage probability.
Geotechnical and Geological Engineering - The effects of diameter and location of drain pipes on the uplift force and exit hydraulic gradient for a gravity dam are investigated. A numerical model... 相似文献
Acta Geochimica - In the last five decades, Rare Earth Elements (REE) are mostly produced from carbonatite complexes and alkaline magmatic environments. In this respect, pyroclastic flows produced... 相似文献
Acta Geotechnica - A systematical testing program on frozen Onsøy clay under isotropic loading and undrained shearing at different temperatures... 相似文献
Acta Geotechnica - In this study, an analytical model is developed to establish a framework for predicting the coefficient of lateral soil pressure in unsaturated soils. To this end, the disturbed... 相似文献
The Upper Quaternary seismic stratigraphy and active faults of the Gulf of İzmit were investigated by means of high-resolution
shallow seismic profiling data in the source region of 1999 İzmit earthquake. High-resolution seismic reflection data correlated
with borehole data indicate that the stratigraphy of İzmit Bay consists of three distinct depositional sequences formed in
response to middle Pleistocene-Holocene sea-level changes. Reflector R, separating the pre-Holocene sequences (1 and 2) from
the Holocene sequence (3), represents an erosional unconformity produced by the subaerial fluvial erosion of the continental
shelves at the time of the last glacial maximum. Occasional, anomalous reflections (acoustic turbidity) observed within the
Holocene sequence are interpreted as gas accumulations. The maximum thickness of the Holocene sediments is found to be about
25 m. The isopach map of Holocene sediment implies that the thickness of the Holocene decreases from the east towards the
central and western basins of İzmit Bay. Two distinct fault systems are interpreted in İzmit Bay. The main fault system extending
roughly in an E-W direction along the Gulf of İzmit is an active right lateral strike slip fault with a normal component.
The secondary faults are normal faults striking in different directions and these are identified as being both active and
inactive. In addition, prominent compressive features are identified in the seismic cross-sections of some profiles acquired
to the east of Hersek Peninsula where the focal mechanisms of the aftershocks of the 1999 İzmit earthquake also reveal predominantly
reverse faulting mechanisms, as identified by a local dense seismic network. 相似文献
In watersheds that have not sufficient meteorological and hydrometric data for simulating rainfall-runoff events, using geomorphologic and geomorphoclimatic characteristics of watershed is a conventional method for the simulation. A number of rainfall-runoff models utilize these characteristics such as Nash-IUH, Clark-IUH, Geomorphologic Instantaneous Unit Hydrograph(GIUH), Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH), GIUH-based Nash(GIUH-Nash) and GcIUH-based Clark(GcIUH-Clark). But all these models are not appropriate for mountainous watersheds. Therefore, the objective of this study is to select the best of them for the simulation. The procedure of this study is: a) selecting appropriate rainfall-runoff events for calibration and validation of six hybrid models, b) distinguishing the best model based on different performance criteria(Percentage Error in Volume(PEV); Percentage Error in Peak(PEP); Percentage Error in Time to Peak(PETP); Root Mean Square Error(RMSE) and Nash-Sutcliffe model efficiency coefficient(ENS)), c) Sensitivity analysis for determination of the most effective parameter at each model, d) Uncertainty determination of different parameters in each model and confirmation of the obtained results by application of the performance criteria. For application of this procedure, the Navrood watershed in the north of Iran as a mountainous watershed has been considered. The results showed that the ClarkIUH and GcIUH-Clark are suitable models for simulation of flood hydrographs, while other models cannot simulate flood hydrographs appropriately. The sensitivity analysis shows that the most sensitive parameters are the infiltration constant rate and time of concentration in the Clark-IUH model. Also, the most sensitive parameters include the infiltration constant rate and storage coefficient in the GcIUHClark model. The Clark-IUH and GcIUH-Clark models are more sensitive to their parameters. The Latin Hypercube Sampling(LHS) on Monte Carlo(MC) simulation method was used for evaluation of uncertainty of data in rainfall-runoff models. In this method 500 sets of data values are produced and then the peak discharge of flood hydrographs for each produced data set is simulated with rainfall-runoff models. The uncertainty of data changes the value of simulated peak discharge of flood hydrograph. The uncertainty analysis shows that the observed peak discharges of different rainfall-runoff events are within the range of values of simulated by the six hybrid rainfall-runoff models and IUH that inputs of these models were the produced data sets. The range of the produced peak discharge of flood hydrographs by the Clark-IUH and GcIUH-Clark models is wider than those of other models. 相似文献
Finding potential sites for resilient prawn production in the tropical environment that also prevents wastage of natural resources is not an easy task. The purpose of this study is to evaluate water quality suitability for prawn farming in Negeri Sembilan of Peninsular Malaysia based on Geographic Information System (GIS). To achieve this goal, numerous criteria including sources of water, water temperature, water pH, sources of pollution, salinity, soil texture and availability of phytoplankton criteria were considered for the modelling process. Analytic Hierarchy Process (AHP) technique was performed to standardize the criteria and the weighting process. The weighted overlay of indicators and results were accomplished by applying the Multi‐Criteria Decision Analysis (MCDA) method in GIS. It was indicated that the Negeri Sembilan area has potential for prawn farming. The results showed that about 25 per cent (163 056.93 ha) of the area was most suitable for prawn farming, about 58 per cent (384 656.88 ha) was considered moderately suitable, while 18 per cent (117 633.49 ha) was regarded as least suitable. The study concluded that the multi‐criteria decision analysis of water quality for prawn farming is vital for regional economic planning in the Negeri Sembilan area and also significant when establishing a model for aquaculture development. 相似文献
Journal of Oceanology and Limnology - The hydrological models and simplified methods of Saint-venant equations are used extensively in hydrological modeling, in particular for the simulation of the... 相似文献
Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.
