Search results for: probabilistic tractography

Authors: Sekkal Nawel

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The Web of Things (WoT), integration of smart technologies from the Internet or network to Web architecture or application, is becoming more complex, larger, and dynamic. The WoT is associated with various elements such as sensors, devices, networks, protocols, data, functionalities, and architectures to perform services for stakeholders. These services operate in the context of the interaction of stakeholders and the WoT elements. Such context is becoming a key information source from which data are of various nature and uncertain, thus leading to complex situations. In this paper, we take interest in the development of intelligent Web services. The key ingredients of this “intelligent” notion are the context diversity, the necessity of a semantic representation to manage complex situations and the capacity to reason with uncertain data. In this perspective, we introduce a multi-layered architecture based on a generic intelligent Web service model dealing with various contexts, which proactively predict future situations and reactively respond to real-time situations in order to support decision-making. For semantic context data representation, we use PR-OWL, which is a probabilistic ontology based on Multi-Entity Bayesian Networks (MEBN). PR-OWL is flexible enough to represent complex, dynamic, and uncertain contexts, the key requirements of the development for the intelligent Web services. A case study was carried out using the proposed architecture for intelligent plant watering to show the role of proactive and reactive contextual reasoning in terms of WoT.

Keywords: smart web service, the web of things, context reasoning, proactive, reactive, multi-entity bayesian networks, PR-OWL

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Authors: Josif Figueroa, Kush Bubbar, Greg Young-Morris

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The reliability evaluation of composite generation and bulk transmission systems is crucial for ensuring a reliable supply of electrical energy to significant system load points. However, evaluating adequacy indices using probabilistic methods like sequential Monte Carlo Simulation can be computationally expensive. Despite this, it is necessary when time-varying and interdependent resources, such as renewables and energy storage systems, are involved. Recent advances in solving power network optimization problems and parallel computing have improved runtime performance while maintaining solution accuracy. This work introduces CompositeSystems, an open-source Composite System Reliability Evaluation tool developed in Julia™, to address the current deficiencies of commercial and non-commercial tools. This work introduces its design, validation, and effectiveness, which includes analyzing two different formulations of the Optimal Power Flow problem. The simulations demonstrate excellent agreement with existing published studies while improving replicability and reproducibility. Overall, the proposed tool can provide valuable insights into the performance of transmission systems, making it an important addition to the existing toolbox for power system planning.

Keywords: open-source software, composite system reliability, optimization methods, Monte Carlo methods, optimal power flow

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Zunaira Asif, Zhi Chen

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The sustainable measures on air quality management are recognized as one of the most serious environmental concerns in the mining region. The mining operations emit various types of pollutants which have significant impacts on the environment. This study presents a stochastic control strategy by developing the air pollution control model to achieve a cost-effective solution. The optimization method is formulated to predict the cost of treatment using linear programming with an objective function and multi-constraints. The constraints mainly focus on two factors which are: production of metal should not exceed the available resources, and air quality should meet the standard criteria of the pollutant. The applicability of this model is explored through a case study of an open pit metal mine, Utah, USA. This method simultaneously uses meteorological data as a dispersion transfer function to support the practical local conditions. The probabilistic analysis and the uncertainties in the meteorological conditions are accomplished by Monte Carlo simulation. Reasonable results have been obtained to select the optimized treatment technology for PM_2.5, PM₁₀, NO_x, and SO₂. Additional comparison analysis shows that baghouse is the least cost option as compared to electrostatic precipitator and wet scrubbers for particulate matter, whereas non-selective catalytical reduction and dry-flue gas desulfurization are suitable for NO_x and SO₂ reduction respectively. Thus, this model can aid planners to reduce these pollutants at a marginal cost by suggesting control pollution devices, while accounting for dynamic meteorological conditions and mining activities.

Keywords: air pollution, linear programming, mining, optimization, treatment technologies

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Authors: Angela U. Makolo, Temitayo A. Olagunju

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The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained using high throughput experimental procedures. However, these high throughput methods are known to produce very high rates of false positive and negative interactions. In order to construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed. A weighted interaction graph of the Saccharomyces cerevisiae (Baker’s Yeast) organism using the proteins as the nodes and interactions between them as edges was constructed. The weights were obtained using Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model. A pathway was formalized as a simple path in the interaction network from a starting protein and an ending protein of interest. We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. cerevisiae.

Keywords: Bayesian networks, protein interaction networks, Saccharomyces cerevisiae, signalling pathways

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Authors: Amir Alani, Mojtaba Mahmoodian, Anna Romanova, Asaad Faramarzi

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Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

Keywords: reliability analysis, service life prediction, Monte Carlo simulation method, first passage probability theory, gamma distributed degradation model

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Authors: Mohammad Amin Tutunchian, Pedram Roshani, Reza Rezvani, Julio Ángel Infante Sedano

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The highly recommended approach to design, known as the load and resistance factor design (LRFD) method, employs the geotechnical resistance factor (GRF) for shaping pile foundation designs. Within the standard process for designing pile foundations, geotechnical engineers commonly adopt a design strategy rooted in saturated soil mechanics (SSM), often disregarding the impact of unsaturated soil behavior. This oversight within the design procedure leads to the omission of the enhancement in shear strength exhibited by unsaturated soils, resulting in a more cautious outcome in design results. This research endeavors to present a methodology for fine-tuning the GRF used for axially loaded driven piles in Winnipeg, Canada. This is achieved through the application of a well-established probabilistic approach known as the first-order second moment (FOSM) method while also accounting for the influence of unsaturated soil behavior. The findings of this study demonstrate that incorporating the influence of unsaturated conditions yields an elevation in projected bearing capacity and recommends higher GRF values in accordance with established codes. Additionally, a novel factor referred to as phy has been introduced to encompass the impact of saturation conditions in the calculation of pile bearing capacity, as guided by prevalent static analysis techniques.

Keywords: unsaturated soils, shear strength, LRFD, FOSM, GRF

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Authors: Feten Chihi, Gabriella Varga

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The world's most prevalent waste management strategy is landfills. However, it grew more difficult due to a lack of acceptable waste sites. In order to develop larger landfills and extend their lifespan, the purpose of this article is to expand the capacity of the construction by varying the slope's inclination and to examine its effect on the safety factor. The capacity change with tilt is mathematically determined. Using a new probabilistic calculation method that takes into account the heterogeneity of waste layers, the safety factor for various slope angles is examined. To assess the effect of slope variation on the overall safety of landfills, over a hundred computations were performed for each angle. It has been shown that capacity increases significantly with increasing inclination. Passing from 1:3 to 2:3 slope angles and from 1:3 to 1:2 slope angles, the volume of garbage that can be deposited increases by 40 percent and 25 percent, respectively, of the initial volume. The results of the safety factor indicate that slopes of 1:3 and 1:2 are safe when the standard method (homogenous waste) is used for computation. Using the new approaches, a slope with an inclination of 2:3 can be deemed safe, despite the fact that the calculation does not account for the safety-enhancing effect of daily cover layers. Based on the study reported in this paper, the malty layered nonhomogeneous calculating technique better characterizes the safety factor. As it more closely resembles the actual state of landfills, the employed technique allows for more flexibility in design parameters. This work represents a substantial advance in limiting both safe and economical landfills.

Keywords: landfill, municipal solid waste, slope inclination, capacity, safety factor

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Authors: Siti Khadijah Che Osmi, Mohammed Ahmad Syed

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Underground structures are crucial components which required detailed analysis and design. Tunnels, for instance, are massively constructed as transportation infrastructures and utilities network especially in urban environments. Considering their prime importance to the economy and public safety that cannot be compromised, thus any instability to these tunnels will be highly detrimental to their performance. Recent experience suggests that tunnels become vulnerable during earthquakes and blast scenarios. However, a very limited amount of studies has been carried out to study and understanding the dynamic response and performance of underground tunnels under those unpredictable extreme hazards. In view of the importance of enhancing the resilience of these structures, the overall aims of the study are to evaluate probabilistic future performance of shallow tunnels subjected to seismic and blast loads by developing detailed fragility analysis. Critical non-linear time history numerical analyses using sophisticated finite element software Midas GTS NX have been presented about the current methods of analysis, taking into consideration of structural typology, ground motion and explosive characteristics, effect of soil conditions and other associated uncertainties on the tunnel integrity which may ultimately lead to the catastrophic failure of the structures. The proposed fragility curves for both extreme loadings are discussed and compared which provide significant information the performance of the tunnel under extreme hazards which may beneficial for future risk assessment and loss estimation.

Keywords: fragility analysis, seismic loads, shallow tunnels, blast loads

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Authors: Enze Zhang

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Large numbers of inter-basin water transfer (IBWT) projects are constructed or proposed all around the world as solutions to water distribution and supply problems. Nowadays, as climate change warms the atmosphere, alters the hydrologic cycle, and perturbs water availability, large scale IBWTs which are sensitive to these water-related changes may carry significant risk. Given this reality, IBWTs have elicited great controversy and assessments of vulnerability to climate change are urgently needed worldwide. In this paper, we consider the South-to-North Water Transfer Project (SNWTP) in China as a case study, and introduce a bottom-up vulnerability assessment framework. Key hazards and risks related to climate change that threaten future water availability for the SNWTP are firstly identified. Then a performance indicator is presented to quantify the vulnerability of IBWT by taking three main elements (i.e., sensitivity, adaptive capacity, and exposure degree) into account. A probabilistic Budyko model is adapted to estimate water availability responses to a wide range of possibilities for future climate conditions in each region of the study area. After bottom-up quantifying the vulnerability based on the estimated water availability, our findings confirm that SNWTP would greatly alleviate geographical imbalances in water availability under some moderate climate change scenarios but raises questions about whether it is a long-term solution because the donor basin has a high level of vulnerability due to extreme climate change.

Keywords: vulnerability, climate change, inter-basin water transfer, bottom-up

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Authors: Nima Pirhadi, Shao Yong Bo, Xusheng Wan, Jianguo Lu, Jilei Hu

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Although gravels and gravelly soils are assumed to be non-liquefiable because of high conductivity and small modulus; however, the occurrence of this phenomenon in some historical earthquakes, especially recently earthquakes during 2008 Wenchuan, Mw= 7.9, 2014 Cephalonia, Greece, Mw= 6.1 and 2016, Kaikoura, New Zealand, Mw = 7.8, has been promoted the essential consideration to evaluate risk assessment and hazard analysis of seismic gravelly soil liquefaction. Due to the limitation in sampling and laboratory testing of this type of soil, in situ tests and site exploration of case histories are the most accepted procedures. Of all in situ tests, dynamic penetration test (DPT), Which is well known as the Chinese dynamic penetration test, and shear wave velocity (Vs) test, have been demonstrated high performance to evaluate seismic gravelly soil liquefaction. However, the lack of a sufficient number of case histories provides an essential limitation for developing new models. This study at first investigates recent earthquakes that caused liquefaction in gravelly soils to collect new data. Then, it adds these data to the available literature’s dataset to extend them and finally develops new models to assess seismic gravelly soil liquefaction. To validate the presented models, their results are compared to extra available models. The results show the reasonable performance of the proposed models and the critical effect of gravel content (GC)% on the assessment.

Keywords: liquefaction, gravel, dynamic penetration test, shear wave velocity

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Authors: Belén Vinaixa Kinnear, Arturo Hidalgo López, Bernardo Elembo Wilasi, Pablo Fernández Pérez, Cecilia Hernández Fuentealba

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The expanded use of risk assessment in legislative and corporate decision-making has increased the role of expert judgement in giving data for security-related decision-making. Expert judgements are required in most steps of risk assessment: danger recognizable proof, hazard estimation, risk evaluation, and examination of choices. This paper presents a fault tree analysis (FTA), which implies a probabilistic failure analysis applied to leakage of oil in a subsea production system. In standard FTA, the failure probabilities of items of a framework are treated as exact values while evaluating the failure probability of the top event. There is continuously insufficiency of data for calculating the failure estimation of components within the drilling industry. Therefore, fuzzy hypothesis can be used as a solution to solve the issue. The aim of this paper is to examine the leaks from the Zafiro West subsea oil facility by using fuzzy fault tree analysis (FFTA). As a result, the research has given theoretical and practical contributions to maritime safety and environmental protection. It has been also an effective strategy used traditionally in identifying hazards in nuclear installations and power industries.

Keywords: expert judgment, probability assessment, fault tree analysis, risk analysis, oil pipelines, subsea production system, drilling, quantitative risk analysis, leakage failure, top event, off-shore industry

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Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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Authors: Mustapha Kamel Mihoubi, Mohamed Essadik Kerkar

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Probabilistic risk analysis models are used to provide a better understanding of the reliability and structural failure of works, including when calculating the stability of large structures to a major risk in the event of an accident or breakdown. This work is interested in the study of the probability of failure of concrete dams through the application of reliability analysis methods including the methods used in engineering. It is in our case, the use of level 2 methods via the study limit state. Hence, the probability of product failures is estimated by analytical methods of the type first order risk method (FORM) and the second order risk method (SORM). By way of comparison, a level three method was used which generates a full analysis of the problem and involves an integration of the probability density function of random variables extended to the field of security using the Monte Carlo simulation method. Taking into account the change in stress following load combinations: normal, exceptional and extreme acting on the dam, calculation of the results obtained have provided acceptable failure probability values which largely corroborate the theory, in fact, the probability of failure tends to increase with increasing load intensities, thus causing a significant decrease in strength, shear forces then induce a shift that threatens the reliability of the structure by intolerable values of the probability of product failures. Especially, in case the increase of uplift in a hypothetical default of the drainage system.

Keywords: dam, failure, limit-state, monte-carlo, reliability, probability, simulation, sliding, taylor

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Authors: J. Thiele

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Teachers working at vocational Colleges are often confronted with the problem, that many students graduated from different schools and therefore each had a different education. Especially in mathematics many students lack fundamentals or had different priorities at their previous schools. Furthermore, these vocational Colleges have to provide Graduations for many different working-fields, with different core themes. The Colleges are interested in measuring the different Education levels of their students and providing assistance for those who need to catch up. The Project mathe-meistern was initiated to remedy this problem at vocational Colleges. For this purpose, online-tests were developed. The aim of these tests is to evaluate basic mathematical abilities of the students. The tests are online Multiple-Choice-Tests with a total of 65 Items. They are accessed online with a unique Transaction-Number (TAN) for each participant. The content is divided in several Categories (Arithmetic, Algebra, Fractions, Geometry, etc.). After each test, the student gets a personalized summary depicting their strengths and weaknesses in mathematical Basics. Teachers can visit a special website to examine the results of their classes or single students. In total 5830 students did participate so far. For standardization and optimization purposes the tests are being evaluated, using the classic and probabilistic Test-Theory regarding Objectivity, Reliability and Validity, annually since 2015. This Paper is about the Optimization process considering the Rasch-scaling and Standardization of the tests. Additionally, current results using standardized tests will be discussed. To achieve this Competence levels and Types of errors of students attending vocational Colleges in Nordrheinwestfalen, Germany, were determined, using descriptive Data and Distractorevaluations.

Keywords: diagnostical tests in mathematics, distractor devaluation, test-optimization, test-theory

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Authors: A. Nicknam, N. Eftekhari, A. Mazarei, M. Ganjvar

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This article presents an alternative collapse capacity intensity measure in the three elements form which is influenced by the spectral ordinates at periods longer than that of the first mode period at near and far source sites. A parameter, denoted by β, is defined by which the spectral ordinate effects, up to the effective period (2T_1), on the intensity measure are taken into account. The methodology permits to meet the hazard-levelled target extreme event in the probabilistic and deterministic forms. A MATLAB code is developed involving OpenSees to calculate the collapse capacities of the 8 archetype RC structures having 2 to 20 stories for regression process. The incremental dynamic analysis (IDA) method is used to calculate the structure’s collapse values accounting for the element stiffness and strength deterioration. The general near field set presented by FEMA is used in a series of performing nonlinear analyses. 8 linear relationships are developed for the 8structutres leading to the correlation coefficient up to 0.93. A collapse capacity near field prediction equation is developed taking into account the results of regression processes obtained from the 8 structures. The proposed prediction equation is validated against a set of actual near field records leading to a good agreement. Implementation of the proposed equation to the four archetype RC structures demonstrated different collapse capacities at near field site compared to those of FEMA. The reasons of differences are believed to be due to accounting for the spectral shape effects.

Keywords: collapse capacity, fragility analysis, spectral shape effects, IDA method

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Hounyèmè Romuald, Mama Daouda, Argillier Christine

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The present study focused on the use of benthic macrofauna to define the reference state of an anthropized lagoon (Nokoué-Benin) from the responses of relevant metrics to proxies. The approach used is a combination of a joint species distribution model and Bayesian networks. The joint species distribution model was used to select the relevant metrics and generate posterior probabilities that were then converted into posterior response probabilities for each of the quality classes (pressure levels), which will constitute the conditional probability tables allowing the establishment of the probabilistic graph representing the different causal relationships between metrics and pressure proxies. For the definition of the reference thresholds, the predicted responses for low-pressure levels were read via probability density diagrams. Observations collected during high and low water periods spanning 03 consecutive years (2004-2006), sampling 33 macroinvertebrate taxa present at all seasons and sampling points, and measurements of 14 environmental parameters were used as application data. The study demonstrated reliable inferences, selection of 07 relevant metrics and definition of quality thresholds for each environmental parameter. The relevance of the metrics as well as the reference thresholds for ecological assessment despite the small sample size, suggests the potential for wider applicability of the approach for aquatic ecosystem monitoring and assessment programs in developing countries generally characterized by a lack of monitoring data.

Keywords: pressure proxies, bayesian inference, bioindicators, acadjas, functional traits

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Authors: Habtamu Tkubet Ebuy

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Purpose Conventional dynamic energy simulation tools go beyond the static dimension of simplified methods by providing better and more accurate prediction of building performance. However, their ability to forecast actual performance is undermined by a low representation of human interactions. The purpose of this study is to examine the potential benefits of incorporating information on occupant diversity into occupant behavior models used to simulate building performance. The co-simulation of the stochastic behavior of the occupants substantially increases the accuracy of the simulation. Design/methodology/approach In this article, probabilistic models of the "opening and closing" behavior of the window of inhabitants have been developed in a separate multi-agent platform, SimOcc, and implemented in the building simulation, TRNSYS, in such a way that the behavior of the window with the interconnectivity can be reflected in the simulation analysis of the building. Findings The results of the study prove that the application of complex behaviors is important to research in predicting actual building performance. The results aid in the identification of the gap between reality and existing simulation methods. We hope this study and its results will serve as a guide for researchers interested in investigating occupant behavior in the future. Research limitations/implications Further case studies involving multi-user behavior for complex commercial buildings need to more understand the impact of the occupant behavior on building performance. Originality/value This study is considered as a good opportunity to achieve the national strategy by showing a suitable tool to help stakeholders in the design phase of new or retrofitted buildings to improve the performance of office buildings.

Keywords: occupant behavior, co-simulation, energy consumption, thermal comfort

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Meriem Bahij, Ahmed Nafidi, Boujemâa Achchab, Sílvio M. A. Gama, José A. O. Matos

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Stochastic modeling concerns the use of probability to model real-world situations in which uncertainty is present. Therefore, the purpose of stochastic modeling is to estimate the probability of outcomes within a forecast, i.e. to be able to predict what conditions or decisions might happen under different situations. In the present study, we present a model of a stochastic diffusion process based on the bi-Weibull distribution function (its trend is proportional to the bi-Weibull probability density function). In general, the Weibull distribution has the ability to assume the characteristics of many different types of distributions. This has made it very popular among engineers and quality practitioners, who have considered it the most commonly used distribution for studying problems such as modeling reliability data, accelerated life testing, and maintainability modeling and analysis. In this work, we start by obtaining the probabilistic characteristics of this model, as the explicit expression of the process, its trends, and its distribution by transforming the diffusion process in a Wiener process as shown in the Ricciaardi theorem. Then, we develop the statistical inference of this model using the maximum likelihood methodology. Finally, we analyse with simulated data the computational problems associated with the parameters, an issue of great importance in its application to real data with the use of the convergence analysis methods. Overall, the use of a stochastic model reflects only a pragmatic decision on the part of the modeler. According to the data that is available and the universe of models known to the modeler, this model represents the best currently available description of the phenomenon under consideration.

Keywords: diffusion process, discrete sampling, likelihood estimation method, simulation, stochastic diffusion process, trends functions, bi-parameters weibull density function

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Authors: Jose Ruiz, Jose Velasquez, Holger Lovon

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Peruvian university buildings are critical structures for which very little research about its seismic vulnerability is available. This paper develops a probabilistic methodology that predicts seismic loss for university buildings with simulated fragility functions. Two university buildings located in the city of Cusco were analyzed. Fragility functions were developed considering seismic and structural parameters uncertainty. The fragility functions were generated with the Latin Hypercube technique, an improved Montecarlo-based method, which optimizes the sampling of structural parameters and provides at least 100 reliable samples for every level of seismic demand. Concrete compressive strength, maximum concrete strain and yield stress of the reinforcing steel were considered as the key structural parameters. The seismic demand is defined by synthetic records which are compatible with the elastic Peruvian design spectrum. Acceleration records are scaled based on the peak ground acceleration on rigid soil (PGA) which goes from 0.05g to 1.00g. A total of 2000 structural models were considered to account for both structural and seismic variability. These functions represent the overall building behavior because they give rational information regarding damage ratios for defined levels of seismic demand. The university buildings show an expected Mean Damage Factor of 8.80% and 19.05%, respectively, for the 0.22g-PGA scenario, which was amplified by the soil type coefficient and resulted in 0.26g-PGA. These ratios were computed considering a seismic demand related to 10% of probability of exceedance in 50 years which is a requirement in the Peruvian seismic code. These results show an acceptable seismic performance for both buildings.

Keywords: fragility functions, university buildings, loss assessment, Montecarlo simulation, latin hypercube

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Authors: Y. Wang, S. Zhang, X. Chen

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The longitudinal compressive strength of fibre reinforced plastic (FRP) possess a large stochastic variability, which limits efficient application of composite structures. This study aims to address how the random fibre packing affects the uncertainty of FRP compressive strength. An novel approach is proposed to generate random fibre packing status by a combination of Latin hypercube sampling and random sequential expansion. 3D nonlinear finite element model is built which incorporates both the matrix plasticity and fibre geometrical instability. The matrix is modeled by isotropic ideal elasto-plastic solid elements, and the fibres are modeled by linear-elastic rebar elements. Composite with a series of different nominal fibre volume fractions are studied. Premature fibre waviness at different magnitude and direction is introduced in the finite element model. Compressive tests on uni-directional CFRP (carbon fibre reinforced plastic) are conducted following the ASTM D6641. By a comparison of 3D FE models and compressive tests, it is clearly shown that the stochastic variation of compressive strength is partly caused by the random fibre packing, and normal or lognormal distribution tends to be a good fit the probabilistic compressive strength. Furthermore, it is also observed that different random fibre packing could trigger two different fibre micro-buckling modes while subjected to longitudinal compression: out-of-plane buckling and twisted buckling. The out-of-plane buckling mode results much larger compressive strength, and this is the major reason why the random fibre packing results a large uncertainty in the FRP compressive strength. This study would contribute to new approaches to the quality control of FRP considering higher compressive strength or lower uncertainty.

Keywords: compressive strength, FRP, micro-buckling, random fibre packing

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Authors: Yo Fukutani, Shuji Moriguchi, Takuma Kotani, Terada Kenjiro

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If risk management of the assets owned by companies, risk assessment of real estate portfolio, and risk identification of the entire region are to be implemented, it is necessary to consider simultaneous damage to multiple buildings. In this research, the Sagami Trough earthquake tsunami that could have a significant effect on the Japanese capital region is focused on, and a method is proposed for simultaneous damage assessment using copulas that can take into consideration the correlation of tsunami depths and building damage between two sites. First, the tsunami inundation depths at two sites were simulated by using a nonlinear long-wave equation. The tsunamis were simulated by varying the slip amount (five cases) and the depths (five cases) for each of 10 sources of the Sagami Trough. For each source, the frequency distributions of the tsunami inundation depth were evaluated by using the response surface method. Then, Monte-Carlo simulation was conducted, and frequency distributions of tsunami inundation depth were evaluated at the target sites for all sources of the Sagami Trough. These are marginal distributions. Kendall’s tau for the tsunami inundation simulation at two sites was 0.83. Based on this value, the Gaussian copula, t-copula, Clayton copula, and Gumbel copula (n = 10,000) were generated. Then, the simultaneous distributions of the damage rate were evaluated using the marginal distributions and the copulas. For the correlation of the tsunami inundation depth at the two sites, the expected value hardly changed compared with the case of no correlation, but the damage rate of the ninety-ninth percentile value was approximately 2%, and the maximum value was approximately 6% when using the Gumbel copula.

Keywords: copulas, Monte-Carlo simulation, probabilistic risk assessment, tsunamis

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Authors: M. Seguini, D. Nedjar

Abstract:

An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability

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Authors: Raed Kouta

Abstract:

A fuel cell is a multi-physical system. Its electrical performance depends on chemical, electrochemical, fluid, and mechanical parameters. Many studies focus on physical and chemical aspects. Our study contributes to the evaluation of the influence of mechanical aspects on the performance of a fuel cell. This study is carried out as part of a reliability approach. Reliability modeling allows to consider the uncertainties of the incoming parameters and the probabilistic modeling of the outgoing parameters. The fuel cell studied is the one often used in land, sea, or air transport. This is the Low-Temperature Proton Exchange Membrane Fuel Cell (PEMFC). This battery can provide the required power level. One of the main scientific and technical challenges in mastering the design and production of a fuel cell is to know its behavior in its actual operating environment. The study proposes to highlight the influence on the production of electrical energy: Mechanical design and manufacturing parameters and their uncertainties (Young module, GDL porosity, permeability, etc.). The influence of the geometry of the bipolar plates is also considered. An experimental design is proposed with two types of materials as well as three geometric shapes for three joining pressures. Other experimental designs are also proposed for studying the influence of uncertainties of mechanical parameters on cell performance. - Mechanical (static, dynamic) and thermal (tightening - compression, vibrations (road rolling and tests on vibration-climatic bench, etc.) loads. This study is also carried out according to an experimental scheme on a fuel cell system for vibration loads recorded on a vehicle test track with three temperatures and three expected performance levels. The work will improve the coupling between mechanical, physical, and chemical phenomena.

Keywords: fuel cell, mechanic, reliability, uncertainties

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Authors: P. V. S. Mascarenhas, B. C. P. Albuquerque, D. J. F. Campos, L. L. Almeida, V. R. Domingues, L. C. S. M. Ozelim

Abstract:

Retaining slope structures are increasingly considered in geotechnical engineering projects due to extensive urban cities growth. These kinds of engineering constructions may present instabilities over the time and may require reinforcement or even rebuilding of the structure. In this context, statistical analysis is an important tool for decision making regarding retaining structures. This study approaches the failure probability of the construction of a retaining wall over the debris of an old and collapsed one. The new solution’s extension length will be of approximately 350 m and will be located over the margins of the Lake Paranoá, Brasilia, in the capital of Brazil. The building process must also account for the utilization of the ruins as a caisson. A series of in situ and laboratory experiments defined local soil strength parameters. A Standard Penetration Test (SPT) defined the in situ soil stratigraphy. Also, the parameters obtained were verified using soil data from a collection of masters and doctoral works from the University of Brasília, which is similar to the local soil. Initial studies show that the concrete wall is the proper solution for this case, taking into account the technical, economic and deterministic analysis. On the other hand, in order to better analyze the statistical significance of the factor-of-safety factors obtained, a Monte Carlo analysis was performed for the concrete wall and two more initial solutions. A comparison between the statistical and risk results generated for the different solutions indicated that a Gabion solution would better fit the financial and technical feasibility of the project.

Keywords: economical analysis, probability of failure, retaining walls, statistical analysis

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Authors: Chun-Hao Lai, Yu-Fang Huang, Pei-Wei Wang, Meng-Han Lin, Mei-Lien Chen

Abstract:

Nonylphenol (NP) is a degradation product of nonylphenol ethoxylates (NPEOs). It is a well-known endocrine disruptor which may cause estrogenic effects. The growing fetus and infants are more vulnerable to exposure to NP than adults. It is important to know the levels and influences of prenatal exposure to NP. The aims of this study were (1) to determine the levels of prenatal exposure among Taiwanese, (2) to evaluate the potential risk for the infants who were breastfed and exposed to NP through the milk. (3) To investigate the correlation between birth outcomes and prenatal exposure to NP. We analyzed thirty one pairs of maternal urines, placentas, first month’ breast milk by high-performance liquid chromatography coupling with fluorescence detector. The questionnaire included socio- demographics, lifestyle, delivery method, dietary and work history. Information about the birth outcomes were obtained from medical records. The daily intake of NP from breast milk was calculated using deterministic and probabilistic risk assessment methods. The geometric means and geometric standard deviation of NP levels in placenta, and breast milk in the first month were 31.2 (1.8) ng/g, 17.2 (1.6) ng/g, respectively. The medium of daily intake NP in breast milk was 1.33 μg/kg-bw/day in the first month. We found negative association between NP levels of placenta and birth height. And we observed negative correlation between maternal urine NP levels and birth weight. In this study, we could provide the NP exposure profile among Taiwan pregnant women and the daily intake of NP in Taiwan infants. Prenatal exposure to higher levels of NP may increase the risk of lower birth weight and shorter birth height.

Keywords: nonylphenol, mother, fetus, placenta, breast milk, urine

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Authors: Tauhidur Rahman, Kasturi Bhuyan

Abstract:

In the present paper, Ground Response Analysis at the Rukni irrigation project has been thoroughly investigated. Surface level seismic hazard is mainly used by the practical Engineers for designing the important structures. Surface level seismic hazard can be obtained accounting the soil factor. Structures on soft soil will show more ground shaking than the structure located on a hard soil. The Surface level ground motion depends on the type of soil. Density and shear wave velocity is different for different types of soil. The intensity of the soil amplification depends on the density and shear wave velocity of the soil. Rukni irrigation project is located in the North Eastern region of India, near the Dauki fault (550 Km length) which has already produced earthquakes of magnitude (Mw= 8.5) in the past. There is a probability of a similar type of earthquake occuring in the future. There are several faults also located around the project site. There are 765 recorded strong ground motion time histories available for the region. These data are used to determine the soil amplification factor by incorporation of the engineering properties of soil. With this in view, three of soil bore holes have been studied at the project site up to a depth of 30 m. It has been observed that in Soil bore hole 1, the shear wave velocity vary from 99.44 m/s to 239.28 m/s. For Soil Bore Hole No 2 and 3, shear wave velocity vary from 93.24 m/s to 241.39 m/s and 93.24m/s to 243.01 m/s. In the present work, surface level seismic hazard at the project site has been calculated based on the Probabilistic seismic hazard approach accounting the soil factor.

Keywords: Ground Response Analysis, shear wave velocity, soil amplification, surface level seismic hazard

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Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi

Abstract:

The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.

Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’

Procedia PDF Downloads 345