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A framework for the generation of bridge-specific fragility curves utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and expensive resimulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of each uncertain variable in their seismic demand model. The methodology is demonstrated through the case study of a multispan concrete bridge class in California. Geometric, material, and structural uncertainties are accounted for in the generation of bridge numerical models and their fragility curves. It is also noted that the traditional lognormality assumption on the demand model leads to unrealistic fragility estimates. Fragility results obtained by the proposed methodology can be deployed in a risk assessment platform such as HAZUS for regional loss estimation. 相似文献
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P. P. Sujith D. Ramanan P. A. LokaBharathi 《Marine Georesources & Geotechnology》2017,35(8):1158-1167
The different mineral phases of the ferromanganese (Fe–Mn) crusts stem from the interaction of biotic and abiotic components. It is therefore vital to study the activity of these components to decipher their contribution to the enrichment/depletion of metals in the crust. Thus, the present study examined sorption and release of Co and Ni by Fe-Mn crusts with associated microbial communities in the presence and absence of the metabolic poison sodium azide (15?mM). The study was conducted in the presence (G+) and absence (G?) of added glucose (0.1%) at temperatures of 4?±?1°C and 28?±?2°C. Results showed that the microbial community had maximal sorption of Co of 66.12?µg?g?1 at 4?±?1°C in the absence of added glucose and 479.75?µg?g?1 at 28?±?2°C in the presence of added glucose. Maximum sorption of Ni in the absence of added glucose was 1.89?µg?g?1 at 4?±?1°C and release of Ni was 51.28?µg?g?1 in the presence of added glucose. Under abiotic conditions with 15?mM sodium azide as a metabolic inhibitor, significant amounts of Co and Ni were released in the G+ medium. Total cell counts on the Fe-Mn crust in the presence of added glucose increased by an order of magnitude from 106 to 107 cells g?1 and in the absence of added glucose remained within the order of 106 cells?g?1 irrespective of temperature of incubation. Microscopic observation of the samples from biotic incubations showed numerous bacterial cells, exopolysaccharides, and structures resembling secondary minerals formed by bacteria. The results indicate that bacteria promote the enrichment of Co and Ni on the hydrogenetic Fe-Mn crusts by sorption processes and release of Ni by reductive dissolution of the oxides. The higher enrichment of Co than Ni is attributed to the way in which microbes interact with the metals. 相似文献
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Many older unreinforced masonry (URM) buildings feature timber floors and solid brick masonry. Simple equivalent frame models can help predicting the expected failure mechanism and estimating the strength of a URM wall. When modelling a URM wall with an equivalent frame model rather than, for example, a more detailed simplified micro-model, the strengths of the piers and spandrels need to be estimated from mechanical or empirical models. Such models are readily available for URM piers, which have been tested in many different configurations. On the contrary, only few models for spandrel strength have been developed. This paper reviews these models, discusses their merits, faults and compares the predicted strength values to the results of recent experimental tests on masonry spandrels. Based on this assessment, the paper outlines recommendations for a new set of strength equations for masonry spandrels. 相似文献
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Acta Geotechnica - Buried pipelines are one of the critical lifeline structures, and recently, efforts have been directed toward their probabilistic risk assessment. This paper explores the... 相似文献
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G. Bala Sujith Krishna Devaraju Narayanappa Long Cao Ken Caldeira Ramakrishna Nemani 《Climate Dynamics》2013,40(7-8):1671-1686
Increasing concentrations of atmospheric CO2 influence climate, terrestrial biosphere productivity and ecosystem carbon storage through its radiative, physiological and fertilization effects. In this paper, we quantify these effects for a doubling of CO2 using a low resolution configuration of the coupled model NCAR CCSM4. In contrast to previous coupled climate-carbon modeling studies, we focus on the near-equilibrium response of the terrestrial carbon cycle. For a doubling of CO2, the radiative effect on the physical climate system causes global mean surface air temperature to increase by 2.14 K, whereas the physiological and fertilization on the land biosphere effects cause a warming of 0.22 K, suggesting that these later effects increase global warming by about 10 % as found in many recent studies. The CO2-fertilization leads to total ecosystem carbon gain of 371 Gt-C (28 %) while the radiative effect causes a loss of 131 Gt-C (~10 %) indicating that climate warming damps the fertilization-induced carbon uptake over land. Our model-based estimate for the maximum potential terrestrial carbon uptake resulting from a doubling of atmospheric CO2 concentration (285–570 ppm) is only 242 Gt-C. This highlights the limited storage capacity of the terrestrial carbon reservoir. We also find that the terrestrial carbon storage sensitivity to changes in CO2 and temperature have been estimated to be lower in previous transient simulations because of lags in the climate-carbon system. Our model simulations indicate that the time scale of terrestrial carbon cycle response is greater than 500 years for CO2-fertilization and about 200 years for temperature perturbations. We also find that dynamic changes in vegetation amplify the terrestrial carbon storage sensitivity relative to a static vegetation case: because of changes in tree cover, changes in total ecosystem carbon for CO2-direct and climate effects are amplified by 88 and 72 %, respectively, in simulations with dynamic vegetation when compared to static vegetation simulations. 相似文献
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B. Sridevi T.V. Ramana MurtyY. Sadhuram M.M.M. RaoK. Maneesha S. Sujith KumarP.L. Prasanna 《Estuarine, Coastal and Shelf Science》2010
Internal waves (IW) are identified off Bhimilipatnam, east coast of India, from the time series CTD (hourly interval) and thermistor chain data (2 min interval) collected during 23–25 Feb 2007. The measurements were carried out at 94 m water depth on the continental shelf edge. These data sets are used to describe the characteristics of IW and their impact on acoustic fields. Garrett and Munk (GM) model has been used to predict the characteristics of low frequency (LF) IW with space and time. Active IW are seen in the layers 54 m–94 m with a velocity of 0.548 km h−1 and the wavelengths of the order of 0.03 km–21.8 km. The model could capture the IW features in the thermocline region accurately than at the bottom. This could be due to the limitation of the model which considers linearity. High frequency IW observed at the bottom could be due to the advection of tidal currents over the shallow irregular bottom in the presence of stratification. The study emphasizes linear IW rather than transient non-linear waves induced by tidal interaction with topography. 相似文献
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Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Recent efforts of regional risk assessment of structures often pose a challenge in dealing with the potentially variable uncertain input parameters. The source of uncertainties can be either epistemic or aleatoric. This article identifies uncertain variables exhibiting strongest influences on the seismic demand of bridge components through various regression techniques such as linear, stepwise, Ridge, Lasso, and elastic net regressions. The statistical results indicate that Lasso regression is the most effective one in predicting the demand model as it has the lowest mean square error and absolute error. As the sensitivity study identifies more than 1 significant variable, a multiparameter fragility model using Lasso regression is suggested in this paper. The proposed fragility methodology is able to identify the relative impact of each uncertain input variable and level of treatment needed for these variables in the estimation of seismic demand models and fragility curves. Thus, the proposed approach helps bridge owners to spend their resources judiciously (e.g., data collection, field investigations, and censoring) in the generation of a more reliable database for regional risk assessment. This proposed approach can be applicable to other structures. 相似文献
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