Abstract: Dependencies between diverse factors involved in probabilistic seismic loss evaluation are recognized to be an imperative issue in acquiring accurate loss estimates. Dependencies among component damage costs could be taken into account considering two partial distinct states of independent or perfectly-dependent for component damage states; however, in our best knowledge, there is no available procedure to take account of loss dependencies in story level. This paper attempts to present a method called "modal cost superposition method" for decoupling story damage costs subjected to earthquake ground motions dealt with closed form differential equations between damage cost and engineering demand parameters which should be solved in complex system considering all stories' cost equations by the means of the introduced "substituted matrixes of mass and stiffness". Costs are treated as probabilistic variables with definite statistic factors of median and standard deviation amounts and a presumed probability distribution. To supplement the proposed procedure and also to display straightforwardness of its application, one benchmark study has been conducted. Acceptable compatibility has been proven for the estimated damage costs evaluated by the new proposed modal and also frequently used stochastic approaches for entire building; however, in story level, insufficiency of employing modification factor for incorporating occurrence probability dependencies between stories has been revealed due to discrepant amounts of dependency between damage costs of different stories. Also, more dependency contribution in occurrence probability of loss could be concluded regarding more compatibility of loss results in higher stories than the lower ones, whereas reduction in incorporation portion of cost modes provides acceptable level of accuracy and gets away from time consuming calculations including some limited number of cost modes in high mode situation.
Abstract: This paper presents a method for steering velocity bounded mobile robots in environments with partially known stationary obstacles. The exact location of obstacles is unknown and only a probability distribution associated with the location of the obstacles is known. Kinematic model of a 2-wheeled differential drive robot is used as the model of mobile robot. The presented control strategy uses the Artificial Potential Field (APF) method for devising a desired direction of movement for the robot at each instant of time while the Constrained Directions Control (CDC) uses the generated direction to produce the control signals required for steering the robot. The location of each obstacle is considered to be the mean value of the 2D probability distribution and similarly, the magnitude of the electric charge in the APF is set as the trace of covariance matrix of the location probability distribution. The method not only captures the challenges of planning the path (i.e. probabilistic nature of the location of unknown obstacles), but it also addresses the output saturation which is considered to be an important issue from the control perspective. Moreover, velocity of the robot can be controlled during the steering. For example, the velocity of robot can be reduced in close vicinity of obstacles and target to ensure safety. Finally, the control strategy is simulated for different scenarios to show how the method can be put into practice.
Abstract: In recent decades, probabilistic constrained optimal
control problems have attracted much attention in many research
fields. Although probabilistic constraints are generally intractable
in an optimization problem, several tractable methods haven been
proposed to handle probabilistic constraints. In most methods,
probabilistic constraints are reduced to deterministic constraints
that are tractable in an optimization problem. However, there is a
gap between the transformed deterministic constraints in case of
known and unknown probability distribution. This paper examines
the conservativeness of probabilistic constrained optimization method
for unknown probability distribution. The objective of this paper is
to provide a quantitative assessment of the conservatism for tractable
constraints in probabilistic constrained optimization with unknown
probability distribution.
Abstract: It is necessary to predict a fatigue crack propagation
life for estimation of structural integrity. Because of an uncertainty
and a randomness of a structural behavior, it is also required to
analyze stochastic characteristics of the fatigue crack propagation life
at a specified fatigue crack size. The essential purpose of this study is to find the effect of load ratio
on probability distribution of the fatigue crack propagation life at a
specified grown crack size and to confirm the good probability
distribution in magnesium alloys under various fatigue load ratio
conditions. To investigate a stochastic crack growth behavior, fatigue
crack propagation experiments are performed in laboratory air under
several conditions of fatigue load ratio using AZ31. By Anderson-Darling test, a goodness-of-fit test for probability
distribution of the fatigue crack propagation life is performed. The
effect of load ratio on variability of fatigue crack propagation life is
also investigated.
Abstract: Today, the need for water sources is swiftly increasing due to population growth. At the same time, it is known that some regions will face with shortage of water and drought because of the global warming and climate change. In this context, evaluation and analysis of hydrological data such as the observed trends, drought and flood prediction of short term flow has great deal of importance. The most accurate selection probability distribution is important to describe the low flow statistics for the studies related to drought analysis. As in many basins In Turkey, Gediz River basin will be affected enough by the drought and will decrease the amount of used water. The aim of this study is to derive appropriate probability distributions for frequency analysis of annual minimum flows at 6 gauging stations of the Gediz Basin. After applying 10 different probability distributions, six different parameter estimation methods and 3 fitness test, the Pearson 3 distribution and general extreme values distributions were found to give optimal results.
Abstract: Wind is among the potential energy resources which
can be harnessed to generate wind energy for conversion into
electrical power. Due to the variability of wind speed with time and
height, it becomes difficult to predict the generated wind energy more
optimally. In this paper, an attempt is made to establish a
probabilistic model fitting the wind speed data recorded at
Makambako site in Tanzania. Wind speeds and direction were
respectively measured using anemometer (type AN1) and wind Vane
(type WD1) both supplied by Delta-T-Devices at a measurement
height of 2 m. Wind speeds were then extrapolated for the height of
10 m using power law equation with an exponent of 0.47. Data were
analysed using MINITAB statistical software to show the variability
of wind speeds with time and height, and to determine the underlying
probability model of the extrapolated wind speed data. The results
show that wind speeds at Makambako site vary cyclically over time;
and they conform to the Weibull probability distribution. From these
results, Weibull probability density function can be used to predict
the wind energy.
Abstract: In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. In this context, most of the studies in the literature can be located in two possible general classes of regression models: models that study the effect the covariates have on the hazard function; and models that study the effect the covariates have on the censored response variable. Proposals in this paper are in the second class of models and, more specifically, on least squares based model approach. Thus, using the bootstrap estimate of the bias, we try to improve the estimation of the regression parameters by reducing their bias, for small sample sizes. Simulation results presented in the paper show that, for reasonable sample sizes and censoring levels, the bias is always smaller for the new proposals.
Abstract: This paper deals with condition monitoring of electric switch machine for railway points. Point machine, as a complex electro-mechanical device, switch the track between two alternative routes. There has been an increasing interest in railway safety and the optimal management of railway equipments maintenance, e.g. point machine, in order to enhance railway service quality and reduce system failure. This paper explores the development of Kolmogorov- Smirnov (K-S) test to detect some point failures (external to the machine, slide chairs, fixing, stretchers, etc), while the point machine (inside the machine) is in its proper condition. Time-domain stator Current signatures of normal (healthy) and faulty points are taken by 3 Hall Effect sensors and are analyzed by K-S test. The test is simulated by creating three types of such failures, namely putting a hard stone and a soft stone between stock rail and switch blades as obstacles and also slide chairs- friction. The test has been applied for those three faults which the results show that K-S test can effectively be developed for the aim of other point failures detection, which their current signatures deviate parametrically from the healthy current signature. K-S test as an analysis technique, assuming that any defect has a specific probability distribution. Empirical cumulative distribution functions (ECDF) are used to differentiate these probability distributions. This test works based on the null hypothesis that ECDF of target distribution is statistically similar to ECDF of reference distribution. Therefore by comparing a given current signature (as target signal) from unknown switch state to a number of template signatures (as reference signal) from known switch states, it is possible to identify which is the most likely state of the point machine under analysis.
Abstract: Rainfall data at fine resolution and knowledge of its
characteristics plays a major role in the efficient design and operation
of agricultural, telecommunication, runoff and erosion control as well
as water quality control systems. The paper is aimed to study the
statistical distribution of hourly rainfall depth for 12 representative
stations spread across Peninsular Malaysia. Hourly rainfall data of 10
to 22 years period were collected and its statistical characteristics
were estimated. Three probability distributions namely, Generalized
Pareto, Exponential and Gamma distributions were proposed to
model the hourly rainfall depth, and three goodness-of-fit tests,
namely, Kolmogorov-Sminov, Anderson-Darling and Chi-Squared
tests were used to evaluate their fitness. Result indicates that the east
cost of the Peninsular receives higher depth of rainfall as compared
to west coast. However, the rainfall frequency is found to be
irregular. Also result from the goodness-of-fit tests show that all the
three models fit the rainfall data at 1% level of significance.
However, Generalized Pareto fits better than Exponential and
Gamma distributions and is therefore recommended as the best fit.
Abstract: Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability distributions. The estimates of the parameters are used to calculate natural disaster forecasts. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.
Abstract: The world economic crises and budget constraints
have caused authorities, especially those in developing countries, to
rationalize water quality monitoring activities. Rationalization
consists of reducing the number of monitoring sites, the number of
samples, and/or the number of water quality variables measured. The
reduction in water quality variables is usually based on correlation. If
two variables exhibit high correlation, it is an indication that some of
the information produced may be redundant. Consequently, one
variable can be discontinued, and the other continues to be measured.
Later, the ordinary least squares (OLS) regression technique is
employed to reconstitute information about discontinued variable by
using the continuously measured one as an explanatory variable. In
this paper, two record extension techniques are employed to
reconstitute information about discontinued water quality variables,
the OLS and the Line of Organic Correlation (LOC). An empirical
experiment is conducted using water quality records from the Nile
Delta water quality monitoring network in Egypt. The record
extension techniques are compared for their ability to predict
different statistical parameters of the discontinued variables. Results
show that the OLS is better at estimating individual water quality
records. However, results indicate an underestimation of the variance
in the extended records. The LOC technique is superior in preserving
characteristics of the entire distribution and avoids underestimation
of the variance. It is concluded from this study that the OLS can be
used for the substitution of missing values, while LOC is preferable
for inferring statements about the probability distribution.