Advancing fracture fragility assessment of pre-Northridge welded column splices

A refined probabilistic assessment of seismic demands and fracture capacity of welded column splice (WCS) connections in welded steel moment resisting frames (WSMRFs) is presented. Seismic demand assessment is performed through cloud-based nonlinear time history analysis (NLTHA) for two case-study structures, i.e., a 4- and a 20- story WSMRFs. Results from NLTHA are used to derive fracture fragility of WCS connections. To this aim, the study investigates (1) optimal ground-motion intensity measures for conditioning probabilistic seismic demand models in terms of global (i.e., maximum inter-story drift ratio) and local (i.e., peak tensile stress in the flange of WCSs) engineering demand parameters of WSMRFs; (2) the effect of ground-motion vertical components on the longitudinal flange stress of WCS connections and their resulting fracture fragility; and (3) the effect of WCS capacity uncertainties on the fracture fragility estimates of those connections. For the latter case, an advanced finite element fracture mechanics-based approach proposed by the authors is employed to capture aleatory and epistemic uncertainties affecting fracture capacities. The focus is on pre-Northridge WCS connections featuring partial joint penetration and brittle materials, making them highly vulnerable to seismic fracture. Fracture fragility results for the case-study structures are compared and discussed, highlighting the importance of the considered issues on fragility estimates, particularly in the case of high-rise structures. Findings from the study contribute shedding some light on the influence of seismic demand and capacity uncertainties on the assessment of fracture fragility of WCS connections. These findings can guide similar performance-based assessment exercises for WSMRFs to inform, for instance, the planning and design of retrofitting strategies for those vulnerable connections.     

Study of land surface temperature and spectral emissivity using multi-sensor satellite data

In this study, an attempt has been made to estimate land surface temperatures (LST) and spectral emissivities over a hard rock terrain using multi-sensor satellite data. The study area, of about 6000 km², is a part of Singhbhum-Orissa craton situated in the eastern part of India. TIR data from ASTER, MODIS and Landsat ETM+ have been used in the present study. Telatemp Model AG-42D Portable Infrared Thermometer was used for ground measurements to validate the results derived from satellite (MODIS/ASTER) data. LSTs derived using Landsat ETM+ data of two different dates have been compared with the satellite data (ASTER and MODIS) of those two dates. Various techniques, viz., temperature and emissivity separation (TES) algorithm, gray body adjustment approach in TES algorithm, Split-Window algorithms and Single Channel algorithm along with NDVI based emissivity approach have been used. LSTs derived from bands 31 and 32 of MODIS data using Split-Window algorithms with higher viewing angle (50°) (LST1 and LST2) are found to have closer agreement with ground temperature measurements (ground LST) over waterbody, Dalma forest and Simlipal forest, than that derived from ASTER data (TES with AST 13). However, over agriculture land, there is some uncertainty and difference between the measured and the estimated LSTs for both validation dates for all the derived LSTs. LST obtained using Single Channel algorithm with NDVI based emissivity method in channel 13 of ASTER data has yielded closer agreement with ground measurements recorded over vegetation and mixed lands of low spectral contrast. LST results obtained with TIR band 6 of Landsat ETM+ using Single Channel algorithm show close agreement over Dalma forest, Simlipal forest and waterbody with LSTs obtained using MODIS and ASTER data for a different date. Comparison of LSTs shows good agreement with ground measurements in thermally homogeneous area. However, results in agriculture area with less homogeneity show difference of LST up to 2°C. The results of the present study indicate that continuous monitoring of LST and emissivity can be undertaken with the aid of multi-sensor satellite data over a thermally homogeneous region.     

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods.     

The REVEALS model,a new tool to estimate past regional plant abundance from pollen data in large lakes: validation in southern Sweden

The REVEALS model was developed to reconstruct quantitatively regional vegetation abundance (in a 10⁴–10⁵ km² area) from pollen assemblages in large lakes (≥100–500 ha). This model corrects for biases in pollen percentages caused by inter‐taxonomic differences in pollen productivity and dispersal. This paper presents the first case study to validate REVEALS, using empirical data from southern Sweden. Percentage cover of modern regional vegetation in Skåne and Småland, two contrasting vegetation regions, was predicted with REVEALS for 26 key taxa, using pollen assemblages from surface sediments in 10 large lakes, and compared to the actual vegetation within 10⁴ km² compiled from satellite data, forestry inventories, crop statistics, aerial photographs, and vegetation inventories. REVEALS works well in predicting the percentage cover of large vegetation units such as total trees (wooded land), total herbs (open land), total conifers and total broad‐leaved trees, and it provides reasonable estimates for individual taxa, including Pinus, Picea, Betula, Corylus, Alnus, Tilia, Salix spp., Juniperus, Poaceae, Cyperaceae, Cerealia and Secale. The results show great potential for REVEALS applications, including (1) quantitative reconstructions of past regional land cover important for palaeoclimatology and nature conservation, and (2) local‐scale reconstruction of vegetation (<1 km² up to ～ 5 km² area) relevant for palaeoecology and archaeology. Copyright © 2007 John Wiley & Sons, Ltd.     

Rare earth element investigation of the Cliefden Outcrop,N.S.W., Australia

The effect of low grade hydrous burial metamorphism (prehnite-pumpellyite facies) upon the rare earth elements (REE) has been studied by using samples from the Cliefden Outcrop, New South Wales. The REE, together with other reputedly immobile elements, have been mobilised during the metamorphism. Although mobile, the REE have behaved remarkably coherently with little light rare earth (LREE) fractionation. This is reflected in the chondrite normalised patterns which are sub-parallel to parallel in shape. High correlations of REE with other elements can be used to predict the maximum likely variation of these elements in the studied outcrop. The high correlations do not necessarily mean that, for similarly metamorphosed terrains, crystallisation-differentiation processes have operated but may rather have resulted from strong geochemical coherence during post-crystallisation elemental redistribution. The REE do not appear to be strongly domain controlled within the Cliefden Outcrop.     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.     

The outcome of the protoplanetary disk of very massive stars

We suggest that planets, brown dwarfs, and even low mass stars can be formed by fragmentation of protoplanetary disks around very massive stars (M ? 100 M_⊙). We discuss how fragmentation conditions make the formation of very massive planetary systems around very massive stars favorable. Such planetary systems are likely to be composed of brown dwarfs and low mass stars of ～0.1–0.3 M_⊙, at orbital separations of ～ few × 100–10⁴ AU. In particular, scaling from solar-like stars suggests that hundreds of Mercury-like planets might orbit very massive stars at ～10³ AU where conditions might favor liquid water. Such fragmentation objects can be excellent targets for the James Webb Space Telescope and other large telescopes working in the IR bands. We predict that deep observations of very massive stars would reveal these fragmentation objects, orbiting in the same orbital plane in cases where there are more than one object.     

Tide versus storm in Epeiric coastal deposition: Two Proterozoic sequences,India

The relative importance of tides and storms in coastal sedimentation in ancient epeiric seas is frequently problematical. Here we appraise the depositional regimes in two Proterozoic Vindhyan formations in India with the aim of elucidating the records of each of these processes. The respective products of the two processes are not easily distinguished as both of them entail repeated fluctuations in water level and depositional energy. Two orders of fluctuation are recognized in both formations. The nature and scale of these two orders of fluctuation along with high-resolution facies analysis make the distinction between the respective products of the two processes possible. Many of the features so long counted as characteristics of tidal rhythms, in the studied formations, exclusively or frequently manifest waxing and waning of storms or fairweather–storm cyclicity. This study highlights the need for reevaluation of ancient coastal sequences in epeiric setting.     

Solid waste management through multi-criteria decision making: using analytic hierarchy process as an assessment framework for the Hooghly district,West Bengal

Solid waste management (SWM) is a crucial service governed by urban local bodies (ULB). Hence, it is essential to identify challenges and opportunities in the SWM procedures and practices towards improved delivery of services. In this study, analytic hierarchy process (AHP) has been applied in the three sub-divisional towns of the Hooghly district, West Bengal (India), namely Chandannagar, Hooghly-Chinsurah and Serampore to analyze the existing SWM scenario. As AHP is a Multi-Criteria Decision Making tool, hence, it has been deployed by experts to come up with SWM performance index, clearly demonstrating the strengths and weaknesses of management strategies in selected study sites. This article further advances the significance of the AHP method by carving out multi-layered realities through the quantification of qualitative insights across various segments of waste management in the three towns. While interviews with waste management officials led to the formulation of key performance indicators and sub-indicators matrix, the obtained normalized weights brought to the fore the real engagement and actions executed by each of these towns in managing solid wastes. The application of this innovative AHP method ensured accuracy in the ranking system across performance of the specified ULBs. This AHP-induced situational analysis of SWM is not only significant in terms of policy formulation in the ULBs of the Hooghly district but has potentials to work at scales.

Graphical abstract