Steady state and liquefaction characteristics of gravely sands

Liquefaction of loose and saturated soils during earthquakes and strong ground motions has been a major cause of damage to buildings and earth embankments as well as other civil engineering structures. In order to evaluate the liquefaction potential and steady state characteristics of gravely sand of south west Tehran,a subsoil exploration program conducted dividing the region into 10 zones. In each zone of 500 m × 500 m a borehole of 20 m deep was drilled. SPT was performed at one meter intervals in each borehole and a total of 200 samples were recovered. Soils of similar grain size distribution have been considered to have similar steady state characteristics,therefore consolidated undrained triaxial tests were performed on these soils of similar grain size distribution to evaluate the steady state strength. The steady state line for each soil type was derived. Comparing the steady state strengths with the shear stress due to an earthquake with a PGA of 0.35 g,the potential of sand liquefaction and .ow failure in soil layers has been evaluated and the settlement of soil due to the liquefaction phenomena is calculated. Finally some recommendations for estimating the steady state strength from simple SPT test in gravely sands are presented.     

Building Information Modelling for High‐rise Land Administration

Current land administration systems mainly use 2D plans to define and secure ownership rights associated with properties in high‐rise buildings. These 2D plans may not effectively communicate and manage the spatial complexity associated with multi‐layered and stacked properties in such buildings; additionally, multiple pages of plans (representing sections of the building) are required to represent all ownership boundaries. In response, land administration organizations have been investigating a 3D digital approach to managing information about ownership rights in high‐rise building structures. In this article, Building Information Modeling (BIM) is proposed as a feasible approach for managing land and property information in high‐rise buildings. BIM provides a collaborative, digital and intelligent 3D data environment for managing building information throughout the lifecycle of buildings. However, there is currently no capacity in BIM for recording and representing information about ownership and boundaries of properties, which is core land administration information. Therefore, this article proposes an extension to the BIM standard, which is implemented in a prototype BIM model of a complex building to showcase the potential capability of using BIM for high‐rise land administration and for modeling 3D ownership rights.     

Soil organic carbon stock as affected by land use/cover changes in the humid region of northern Iran

This study was conducted to determine the changes in the soil carbon stocks as influenced by land use in a humid zone of Deylaman district(10,876 ha), a mountainous region of northern Iran. For this, land use maps were produced from TM and ETM+ images for 1985, 2000 and 2010 years; and this was supplemented by field measurement of soil carbon in 2010. The results showed that the mean soil organic carbon(SOC) density was 6.7±1.8 kg C m-2, 5.2±3.4 kg C m-2 and 3.2±1.8 kg C m-2 for 0-20 cm soil layer and 4.8±1.9 kg C m-2, 3.1±2 kg C m-2 and 2.7±1.8 kg C m-2 for 20-40 cm soil layer in forest, rangeland and cultivated land, respectively. During the past 25 years, 14.4% of the forest area had been converted to rangeland; and 28.4% of rangelands had been converted to cultivated land. According to the historical land use changes in the study area, the highest loss of SOC stocks resulted from the conversion of the forest to rangeland(0.45×104 Mg C in 0-40 cm depth layer); and the conversion of rangeland to cultivated land(0.37×104 Mg C in 0-40 cm), which typically led to the loss of soil carbon in the area studied. The knowledge on the historical land use changes and its influence on overall SOC stocks could be helpful for making management decision for farmers and policy managers in the future, for enhancing the potential of C sequestration in northern Iran.     

Seismic assessment of concrete gravity dams using capacity estimation and damage indexes

A new concept to determine state of the damage in concrete gravity dams is introduced. The Pine Flat concrete gravity dam has been selected for the purpose of the analysis and its structural capacity, assuming no sliding plane and rigid foundation, has been estimated using the two well‐known methods: nonlinear static pushover (SPO) and incremental dynamic analysis (IDA). With the use of these two methods, performance and various limit states of the dam have been determined, and three damage indexes have been proposed on the basis of the comparison of seismic demands and the dam's capacity. It is concluded that the SPO and IDA can be effectively used to develop indexes for seismic performance evaluation and damage assessment of concrete gravity dams. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of inverse geochemical modelling for predicting surface water chemistry in Ekbatan watershed,Hamedan, western Iran

ABSTRACT

A study of surface water chemistry evolution was conducted by multivariate statistical analysis and inverse geochemical modelling using the PHREEQC computer program. Using hierarchical cluster analysis the 14 sampling sites were classified into three groups (recharge, transition and discharge areas). Water chemistry changed along a flow path so that waters with Ca–HCO₃ and Mg–Cl composition changed to Mg–Cl–HCO₃ waters. The order of abundance of the major cations was Mg > Ca > Na > K. Their average concentrations were 21, 19, 3.6 and 2.5 mg L^-1, respectively. Inverse geochemical modelling along flow paths indicated that the dissolution of sylvite and kaolinite, and precipitation of feldspars and andalusite, happened with Na entering the solution and Ca, Mg and K leaving the solution.

Editor D. Koutsoyiannis; Associate editor not assigned     

Database extension for digital soil mapping using artificial neural networks

Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.