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Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid...
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Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.
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The present paper describes the application of GSA (Global Sensitivity Analysis) techniques to mathematical models of bioprocesses in order to rank inputs such as feed titres, flow rates and matrix capacities for the relative infl...
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The present paper describes the application of GSA (Global Sensitivity Analysis) techniques to mathematical models of bioprocesses in order to rank inputs such as feed titres, flow rates and matrix capacities for the relative influence that each exerts upon outputs such as yield or throughput. GSA enables quantification of both the impact of individual variables on process outputs, as well as their interactions. These data highlight those attributes of a bioprocess which offer the greatest potential for achieving manufacturing improvements. Whereas previous GSA studies have been limited to individual unit operations, this paper extends the treatment to an entire downstream process and illustrates its utility by application to the production of a Fab-based rattlesnake antivenom called CroFab (TM) [(Crotalidae Polyvalent Immune Fab (Ovine); Protherics U.K. Limited]. Initially, hyperimmunized ovine serum containing rattlesnake antivenom IgG (product), other antibodies and albumin is applied to a synthetic affinity ligand adsorbent column to separate the antibodies from the albumin. The antibodies are papain-digested into Fab and Fc fragments, before concentration by ultrafiltration. Fc, residual IgG and albumin are eliminated by an ion-exchanger and then CroFab-specific affinity chromatography is used to produce purified antivenom. Application of GSA to the model of this process showed that product yield was controlled by IgG feed concentration and the synthetic-material affinity column's capacity and flow rate, whereas product throughput was predominantly influenced by the synthetic material's capacity, the ultrafiltration concentration factor and the CroFab affinity flow rate. Such information provides a rational basis for identifying the most promising strategies for delivering improvements to commercial-scale biomanufacturing processes.
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Regulatory agencies have a strong interest in sensitivity analysis for the evaluation of physiologically-based pharmacokinetic (PBPK) models used in pharmaceutical research and drug development and regulatory submissions. One of t...
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Regulatory agencies have a strong interest in sensitivity analysis for the evaluation of physiologically-based pharmacokinetic (PBPK) models used in pharmaceutical research and drug development and regulatory submissions. One of the applications of PBPK is the prediction of fraction absorbed and bioavailability for drugs following oral administration. In this context, we performed a variance based global sensitivity analysis (GSA) on in-house PBPK models for drug absorption, with the aim of identifying key parameters that influence the predictions of the fraction absorbed and the bioavailability for neutral, acidic and basic compounds. This analysis was done for four different classes of drugs, defined according to the Biopharmaceutics Classification System, differentiating compounds by permeability and solubility. For class I compounds (highly permeable, highly soluble), the parameters that mainly influence the fraction absorbed are related to the formulation properties, for class II compounds (highly permeable, lowly soluble) to the dissolution process, for class III (lowly permeable, highly soluble) to both absorption process and formulation properties and for class IV (lowly permeable, lowly soluble) to both absorption and dissolution processes. Considering the bioavailability, the results are similar to those for the fraction absorbed, with the addition that parameters related to gut wall and liver clearance influence as well the predictions. This work aimed to give a demonstration of the GSA methodology and highlight its importance in improving our understanding of PBPK absorption models and in guiding the choice of parameters that can safely be assumed, estimated or require data generation to allow informed model prediction.
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Water mains are indispensable infrastructures in many countries around the world. Several factors may be responsible for the failure of these essential pipelines that negatively impact their integrity and service life. The purpose...
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Water mains are indispensable infrastructures in many countries around the world. Several factors may be responsible for the failure of these essential pipelines that negatively impact their integrity and service life. The purpose of this study is to propose models that can predict the average time to failure of water mains by using intelligent approaches, including artificial neural network (ANN), ridge regression (l2), and ensemble decision tree (EDT) models. The developed models were trained by using collected data from Quebec City water mains, including records of the possible factors, such as the materials, length, and diameter of pipes, that contributed to the failure. The ensemble learning model was applied by using a boosting technique to improve the performance of the decision tree model. All models, however, were able to predict reasonably the failure of water mains. A global sensitivity analysis (GSA) was then conducted to test the robustness of the model and to show clearly the relationship between the input and output of the model. The GSA results show that gray cast iron (CI), hyprescon/concrete (Hy), and ductile iron with lining (DIL) are the most vulnerable materials for the model output. The results also indicate that the failure of water mains mostly depends on pipe material and length. It is hoped that this study will help decision makers to avoid unexpected water main failure. (c) 2020 American Society of Civil Engineers.
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The role of global sensitivity analysis (GSA) is to quantify and rank the most influential features for biophysical variable estimation. In this letter, an approximation model, called high-dimensional model representation (HDMR), ...
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The role of global sensitivity analysis (GSA) is to quantify and rank the most influential features for biophysical variable estimation. In this letter, an approximation model, called high-dimensional model representation (HDMR), is utilized to develop a regression method in conjunction with a GSA in the context of determining key input drivers in the estimation of crop biophysical variables from polarimetric synthetic aperture radar data. A multitemporal Radarsat-2 data set is used for the retrieval of three biophysical variables of barley: leaf area index, normalized difference vegetation index, and Biologische Bundesanstalt, Bundessortenamt and CHemische Industrie stage. The HDMR technique is first adopted to estimate a regression model with all available polarimetric features for each biophysical parameter, and sensitivity indices of each feature are then derived to explain the original space with a smaller number of features in which a final regression model is established. To evaluate the applicability of this methodology, root-mean square and coefficient of determination were performed under different amounts of samples. Results highlight that HDMR can be used effectively in biophysical variable estimation for not only reducing computational cost but also for providing a robust regression.
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It is well known that there are many uncertainties in slope engineering, which have great impacts on slope stability models. However, the uncertainty importance measure is rarely applied in slope stability analysis. In this paper,...
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It is well known that there are many uncertainties in slope engineering, which have great impacts on slope stability models. However, the uncertainty importance measure is rarely applied in slope stability analysis. In this paper, the moment-independent method of the uncertainty importance measure is first developed to analyze slope stability, in which the low deviation sequence of Sobol is used to simulate the geotechnical parameter samples, and a nonparametric method of the kernel density estimate is employed to estimate the probability density functions of the output responses. In addition, the Gaussian copula is applied to construct the joint distribution of the shear strength parameters in the uncertainty importance measure of the correlation parameters. Two examples are shown to indicate that the impacts of geotechnical parameters on slope stability models are greatly different. The importance sequences obtained by the moment-independent method are in good agreement with those obtained by the variance-based Monte Carlo method. The importance measure indexes clearly state that the uncertainty of the geotechnical parameters significantly affects slope stability models, and the influence of the negative correlation between the shear strength parameters on the uncertainty importance measure should not be neglected.
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Understanding the development of northern peatlands and their carbon accumulation dynamics is crucial in order to confidently integrate northern peatlands into global carbon cycle models. To achieve this, northern peatland models ...
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Understanding the development of northern peatlands and their carbon accumulation dynamics is crucial in order to confidently integrate northern peatlands into global carbon cycle models. To achieve this, northern peatland models are becoming increasingly complex and now include feedback processes between peat depth, decomposition, hydrology, and vegetation composition and productivity. Here we present results from a global sensitivity analysis performed to assess the behavior and parameter interaction of a peatland simulation model. A series of simulations of the Holocene Peat Model were performed with different parameter combinations in order to assess the role of parameter interactions on the simulated total carbon mass after 5000 years of peatland development. The impact of parameter uncertainty on the simulation results is highlighted, as is the importance of multiple parameter interactions. The model sensitivity indicates that peat physical properties play an important role in peat accumulation; these parameters are poorly constrained by observations and should be a focus of future research. Furthermore, the results show that autogenic processes are able to produce a wide range of peatland development behaviors independently of any external environmental changes. Key Points Peat physical properties are key processes and should be further investigatedAutogenic processes can lead to various peatland development patternsLawn and hummock Sphagnum PFTs are able to modify carbon accumulation dynamics
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In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain i...
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In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost.
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A new method named cluster-based GSA is proposed to enhance the sensitivity analysis of models with temporal or spatial outputs. It is based on a tight coupling between Global Sensitivity Analysis (GSA) and clustering procedures. ...
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A new method named cluster-based GSA is proposed to enhance the sensitivity analysis of models with temporal or spatial outputs. It is based on a tight coupling between Global Sensitivity Analysis (GSA) and clustering procedures. Clustering is introduced to characterize the different behaviors of the model outputs by grouping them into clusters. The cluster-based GSA produces variance-based indices that quantify how the model inputs drive the model outputs toward a given cluster or how they influence variation along a direction defined by two clusters. Aggregated indices are proposed to summarize the overall influence of model inputs on changes of clusters. The method is applied on two models having temporal outputs: a toy example and an environmental model simulating the decomposition of soil organic matter (CANTIS). In both cases, the influence of the model inputs on the different behaviors of model outputs was efficiently reported by this approach.
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In this work, global sensitivity analysis (GSA) was used in conjunction with polynomial chaos expansion (PCE) to help assess unsaturated soil hydraulic parameters from experimental drainage data. The temporal sensitivity of differ...
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In this work, global sensitivity analysis (GSA) was used in conjunction with polynomial chaos expansion (PCE) to help assess unsaturated soil hydraulic parameters from experimental drainage data. The temporal sensitivity of different outputs (cumulative outflow, water content, and pressure head at different positions inside the column) to the Mualem–van Genuchten parameters was analyzed. In addition, the marginal effects of the parameters on the different outputs were analyzed across their entire uncertainty range. The results of the GSA revealed the necessary data set for a successful calibration of the hydraulic parameters. Parameter estimation was then performed in a Bayesian framework usi ng a Markov chain Monte Carlo sampler with the PCEs instead of the discretized Richards’ equation model. The obtained parameters were validated by comparing the whole set of measurements to the results of the calibrated numerical model.
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