Proposed Prediction Procedure Research Articles

Effects of wind-barrier parameters on dynamic responses of wind-road vehicle–bridge system

Accidents, such as deflections, side slips, and even rolls, can easily occur when road vehicles moving on the bridge deck at high speed are subjected to sudden changes owing to strong crosswinds. Wind barriers are an effective way to ensure the driving safety of vehicles on the bridge but with potential negative influences on the aerodynamic performance. Therefore, it is critical to evaluate the shielding effect of wind barriers on road vehicles by assessing the overall impact on the vehicle–bridge system. To optimize the wind-barrier scheme, a series of wind tunnel tests is conducted to evaluate the effects of barrier parameters on the aerodynamic characteristics of the wind-vehicle-bridge system. In this study, the aerodynamic force coefficients of the wind–vehicle–bridge system installed with wind barriers with four different heights and three ventilation ratios were investigated. With wind barriers installed on the bridge deck, dynamic responses of the wind–vehicle–bridge system were simulated with the proposed prediction procedures as those used in an earlier study. Results show that wind barriers on the bridge deck have significant influences on the aerodynamic force coefficients of the bridge, especially on the side-force coefficients, and on the dynamic responses of the wind–vehicle–bridge system. Additionally, the increase of the barrier height and the decrease of the barrier ventilation ratio considerably increase and decrease the lateral and torsional displacements of the bridge and the vehicles, respectively, but with only minor influence on the vertical displacements of both bridges and vehicles. Therefore, it is necessary to include the wind barrier in both experimental and numerical simulations of the wind-road vehicle-bridge system to explore the effect of different barrier parameters on the wind-vehicle-bridge system.

Evaluation of Fatigue Life for TC4 Notched Components Under Variable Amplitude Multiaxial Loading

Fatigue tests under variable amplitude multiaxial loading were conducted on notched specimens of titanium alloy TC4. The key issues of fatigue life estimation under variable amplitude multiaxial loading for notched components have been researched. Multiaxial fatigue critical area of notched specimen was determined first. Then, fatigue damage control parameter based on Wu–Hu–Song model and rainflow cycle counting as well as Miner–Palmgren rule was applied in prediction approach. The proposed prediction procedures were checked against the test data of TC4 notched specimens under variable amplitude multiaxial loading. The prediction results are all within a factor of two scatter band of the test results.

Predicting analysis times in randomized clinical trials with cancer immunotherapy.

BackgroundA new class of immuno-oncology agents has recently been shown to induce long-term survival in a proportion of treated patients. This phenomenon poses unique challenges for the prediction of analysis time in event-driven studies. If the phenomenon of long-term survival is not accounted for properly, the accuracy of the prediction based on the existing methods may be substantially compromised.MethodsParametric mixture cure rate models with the best fit to empirical clinical trial data were proposed to predict analysis times in immuno-oncology studies during the course of the study. The proposed prediction procedure also accounts for the mechanism of action introduced by cancer immunotherapies, such as delayed and long-term survival effects.ResultsThe proposed methodology was retrospectively applied to a randomized phase III immuno-oncology clinical trial. Among various parametric mixture cure rate models, the Weibull cure rate model was found to be the best-fitting model for this study. The unique survival kinetics of cancer immunotherapy was captured in the longitudinal predictions of the final analysis times.ConclusionsParametric mixture cure rate models, along with estimated long-term survival rates, probabilities of study incompletion, and expected statistical powers over time, provide immuno-oncology clinical trial researchers with a useful tool for continuous event monitoring and prediction of analysis times, such that informed decisions with quantifiable risks can be made for better resource and logistic planning.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0117-3) contains supplementary material, which is available to authorized users.

Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters

We address lossy compression of noisy remote sensing images, where the noise is supposed to be spatially uncorrelated (white), additive originally or after a proper variance-stabilizing transformation (VST). In such situations, the so-called optimal operation point (OOP) might exist. The OOP is associated with the parameter that controls compression (e.g., quantization step) for which the compressed image is the closest to the noise-free image according to a certain criterion and is closer than the original noisy image. Lossy compression in the neighborhood of OOP, if it exists, relates to an essential noise filtering effect and some distortions. Then such lossy compression (in the neighborhood of OOP) becomes expedient. However, it may be that OOP does not exist for a given image and the observed noise intensity. In such a situation, it can be reasonable to carry out a more “careful” image compression (with a lower compression ratio). Also, it is expedient to predict the existence of OOP and the compression parameters at this point in advance in order to perform adaptive and automated compression. The OOP existence that can be predicted for some coders based on the discrete cosine transform (DCT) is shown. The proposed prediction procedure is simple and fast. It presumes the calculation of DCT coefficient statistics in nonoverlapping 8×8 pixel blocks for a given image and uses an approximating curve obtained in advance. It is shown that it is possible to predict values for both conventional metrics, such as mean square error or peak-signal-to-noise ratio, and some visual quality metrics for the coder parameters that correspond to a possible OOP. The designed prediction procedure is tested on Hyperion and AVIRIS hyperspectral remote sensing data.

A method for predicting mechanical behaviour of HPJG-Anchors – Part II: Prediction procedure, verifications and parametric studies

A new type of anchorage structure, anchor with an underreamed-segment formed by High Pressure Jet Grouting (HPJG-Anchor), has been extensively used in practice for the anti-floating of underground structures, support of foundation pits and protection of slopes. However, its load–displacement behaviour is seldom researched and not well-understood. Based on the load transfer models pre-proposed in a companion paper, a nonlinear load-transfer method is developed to predict the load–displacement behaviour of HPJG-Anchors, and the specific calculation procedure is also presented. This proposed method is then validated by comparing the calculated results with the field test data, and it is proved that the proposed prediction procedure is capable of providing rational estimation provided that the soil parameters are reliably determined. Afterwards, a series of parametric studies is carried out. The results indicate: the length of the free-stress segment is of an optimal value corresponding to the most efficient displacement control effect; an excessively strengthened free-stress segment will lead to obvious increase of construction cost but little increase of displacement control effect; bearing capacity of an HPJG-Anchor increases nonlinearly with the soil frictional angle and approximately linearly with the soil cohesion and shear modulus.

Effect of fiber orientation on Mode I fracture toughness of CFRP

AbstractThe effect of fiber orientations on fracture toughness of carbon fiber reinforced plastics (CFRP) in Mode I loading was investigated using double cantilever beam (DCB) specimens, based on mesoscopic mechanics. Mesoscopic interlaminar fracture toughness of 0//0 interphase of CFRP was evaluated with mesoscopic finite element models using experimental data. The fracture surface roughness was observed by confocal laser scanning microscopy. Then the mesoscopic interlaminar fracture toughness of CFRP was correlated with the fracture surface roughness. Additionally, the change of the Mode I macroscopic fracture toughness of CFRP was experimentally measured with changing the numbers of 0 and ±θ layers of DCB specimens. The correlation between the fracture toughness of 0//0 and θ//−θ interphases was discussed and a novel procedure was proposed to predict the macroscopic fracture toughness of θ//−θ interphase using finite element method (FEM). The fracture toughness of θ//−θ interphase analyzed by FEM was finally compared with the experimental results to verify the proposed prediction procedure. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

Prognostics of machine condition using soft computing

This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.

Pavement Condition Prediction Using Clusterwise Regression

In this paper, the clusterwise regression model, which fits data to more than one curve, was introduced to model the deterioration of pavement condition. To make the model solvable in practice, a modification was made to the model by the membership of a pavement to each cluster being estimated with the fuzzy sets concept with the corresponding errors. The number of unknowns in the modified model is reduced significantly. The model was then extended to be applicable to cases in which more than two clusters or nonlinear equations were used. Based on the result of the modified clusterwise regression model, a procedure was proposed to predict the pavement condition rating (PCR) for any individual pavement, given that the pavement condition rating at the present age was known. In the example, the ordinary least squares regression method was first employed to determine the PCR prediction curve for a pavement group. The PCRs of the individual pavements were predicted by an adjusted prediction curve based on the prediction curve of the group. The proposed prediction procedure was then applied to the same data set to make the predictions. The results showed that the proposed procedures using the modified clusterwise regression method could result in a smaller prediction error and thus produce a more accurate prediction than one produced with the adjusted curve. Therefore, to improve the accuracy of the pavement condition predictions, the modified clusterwise regression model is recommended in cases in which the pavement families are not well defined.

Pavement Condition Prediction Using Clusterwise Regression

In this paper, the clusterwise regression model, which fits data to more than one curve, was introduced to model the deterioration of pavement condition. To make the model solvable in practice, a modification was made to the model by the membership of a pavement to each cluster being estimated with the fuzzy sets concept with the corresponding errors. The number of unknowns in the modified model is reduced significantly. The model was then extended to be applicable to cases in which more than two clusters or nonlinear equations were used. Based on the result of the modified clusterwise regression model, a procedure was proposed to predict the pavement condition rating (PCR) for any individual pavement, given that the pavement condition rating at the present age was known. In the example, the ordinary least squares regression method was first employed to determine the PCR prediction curve for a pavement group. The PCRs of the individual pavements were predicted by an adjusted prediction curve based on the prediction curve of the group. The proposed prediction procedure was then applied to the same data set to make the predictions. The results showed that the proposed procedures using the modified clusterwise regression method could result in a smaller prediction error and thus produce a more accurate prediction than one produced with the adjusted curve. Therefore, to improve the accuracy of the pavement condition predictions, the modified clusterwise regression model is recommended in cases in which the pavement families are not well defined.

Nonparametric prediction intervals for the future rainfall records

AbstractPrediction of records plays an important role in the environmental applications, especially, prediction of rainfall extremes, highest water levels, sea surface, and air record temperatures. In this paper, based on the observed records drawn from a sequence sample of independent and identically random variables, we develop prediction intervals as well as prediction upper and lower bounds for records from another independent sequence. We extend the prediction problem to include prediction regions for joint upper records from a future sequence sample. The Bonferouni's inequality is used to choose appropriate prediction coefficients for the joint prediction. A real data set representing the records of the annual (January 1–December 31) rainfall at Los Angeles Civic Center is addressed to illustrate the proposed prediction procedures in the environmental applications. Copyright © 2005 John Wiley & Sons, Ltd.

Bayesian prediction of temperature records using the Pareto model

AbstractStatistical prediction of record values has potential environmental applications dealing, for example, with abrupt climate jumps, such as the prediction of rainfall extremes, highest water levels and sea surface or air record temperatures. In this article, and on the basis of observed Pareto records drawn from a sequential sample of independent and identically distributed random variables, we address the problem of Bayesian prediction of future records. The Bayesian predictive distribution is developed for future records and the corresponding highest posterior density (HPD)‐prediction intervals are established. A data set representing the record values of average July temperatures in Neuenburg, Switzerland, is used to illustrate the proposed prediction procedure's environmental application. Copyright © 2004 John Wiley & Sons, Ltd.

Seismic performance evaluation of steel arch bridges against major earthquakes. Part 2: simplified verification procedure

AbstractThe performance‐based philosophy has been accepted as a more reasonable design concept for engineering structures. For this purpose, capacity evaluation and demand prediction procedures for civil engineering structures under earthquake excitations are of great significance. This work presents a displacement‐based seismic performance verification procedure including capacity and seismic demand predictions for steel arch bridges and investigates its applicability. Pushover analyses is employed as a basis in this method to investigate the structure's behaviors. A failure criterion for steel members accounting for the effect of local buckling is involved and an equivalent single‐degree‐of‐freedom (ESDOF) system with a simplified bilinear hysteretic model formulated using pushover analyses results is introduced to estimate the displacement capacity and maximum demand of steel arch bridges under major earthquakes. To check the accuracy of the proposed method, seismic capacities and demands from multi‐degree‐of‐freedom (MDOF) time‐history analyses with Level‐II design earthquake record inputs modeling major earthquakes are used as benchmarks for comparison. By a case study, it is clarified that the proposed prediction procedure can give accurate estimations of displacement capacities and demands of the steel arch bridge in the transverse direction, while insufficient for the longitudinal direction, which confirms the conclusion drawn in other structure types about the applicability of pushover analyses. Copyright © 2004 John Wiley & Sons, Ltd.

E305 INVESTIGATION ON NON-FOURIER HEAT TRANSFER EFFECT FOR COAL PARTICLES UNDER HIGH HEATING RATE

In conventional pulverized coal fired furnaces, the coal particles with mean diameter about 50 μm can be heated up at the rate of (10)^3∿(10)^4℃/s. Heat diffusion performance in rapidly heated coal particles, characterized by the porous structure and rough surface, cannot be reasonably described by the classical Fourier heat diffusion theory. A novel method based on Non-Fourier heat diffusion theory for analyzing the rapid heating up process of coal particles before volatile evolution commences is proposed. The advantages of the proposed prediction procedures over the previous analyzing methods are discussed. Two different models based on Non-Fourier heat conduction theory are applied, and the predicted surface temperature histories of D-P-L model present good agreement with the available measured results.

Damaging properties of ground motions and prediction of maximum response of structures based on momentary energy response

AbstractDynamic damaging potential of ground motions must be evaluated by the response behaviour of structures, and it is necessary to indicate what properties of ground motions are most appropriate for evaluation. For that purpose, the behaviour of energy input process and hysteretic energy dissipation are investigated in this study. It is found that the momentary input energy that is an index for the intensity of input energy is related to the characteristics of earthquakes such as cyclic or impulsive, and to the response displacement of structures immediately.On the basis of these results, a procedure is proposed to predict inelastic response displacement of structures by corresponding earthquake input energy to structural dissipated damping and hysteretic energy. In this procedure the earthquake response of structures is recognized as an input and dissipation process of energy, and therefore structural properties and damaging properties of ground motions can be taken into account more generally.Lastly, the studies of the pseudodynamic loading test of reinforced concrete structure specimens subjected to ground motions with different time duration are shown. The purpose of this test is to estimate the damaging properties of ground motions and the accuracy of the proposed prediction procedure. Copyright © 2002 John Wiley & Sons, Ltd.

ON THE PREDICTION OF FUTURE FAILURES FOR A REPAIRABLE EQUIPMENT SUBJECT TO OVERHAULS

This paper deals with the prediction, from a Bayes viewpoint, of future failures for a repairable equipment subjected both to minimal repairs and periodic overhauls. The effect of major overhauls on the reliability of the equipment is modeled by a proportional age reduction model, while the failure process between two successive overhaul epochs is modeled by the power law process. Prediction both of the future failure times and of the number of failures in a future time interval are provided on the basis of the observed data and of a number of suitable prior densities, which reflect different degrees of belief on the failure mechanism and overhaul effectiveness. Finally, a numerical application illustrates the proposed prediction procedures and their use in assessing the adequacy of the model to describe the observed data set.

Rolling learning-prediction of product formation in bioprocesses

A rolling learning-prediction approach based on neural networks is proposed with the aim of on-line prediction of the product formation. Commercial-scale penicillin cultivations were taken as an example to test the product predictor. Raw data are pretreated in such a way that each input vector of the neural network consists of a series of time-discretised values on a specified transient of process variables. The output vector is composed of the amount of product at the next one and two prediction steps. The process variables involved in the predictor include carbon dioxide and product formation as well as oxygen, precursor and substrate consumption. Accumulated rather than instant values of these variables were used. A simple three-layer feedforward backpropagation neural network with a tangent sigmoidal transfer function in the hidden nodes and a linear one in the output nodes was used as the main frame of the product predictor. The proposed prediction procedure is called rolling learning-prediction because the training database is updated after each sampling interval and the learning-prediction is repeated thereafter. The robustness of the predictor was illustrated by its adaptive ability to widely scattered data sets and extra added noises. The testing results indicated that a prediction accuracy of 2–5% could be generally expected in the later phase of cultivation and reliable prediction time spans may take more than 10% of the cultivation period for penicillin production. An intrinsic problem of using neural networks—occasional trap of the network in bad local minima—is automatically detected and remedied. In addition, it was illustrated by example that the prediction error signal may be potentially used to detect extraordinary charges caused, for example, by contamination. Problems associated with the industrial application of the predictor are discussed.

Predicting variety composite means without diallel crossing

Prediction of variety composite means was shown to be feasible without diallel crossing the parental varieties. Thus, the predicted mean for a quantitative trait of a composite is given by: Yk = a1 sigmaVj + a2sigmaTj + a3<IMG SRC="Image156.gif" WIDTH=16 HEIGHT=18> - a4<IMG SRC="Image157.gif" WIDTH=14 HEIGHT=17>, with coefficients a1 = (n - 2k)/k²(n - 2); a2 = 2n(k - 1)/k²(n - 2); a3 = n(k - 1)/k(n - 1)(n - 2); and a4 = n²(k - 1)/k(n - 1)(n - 2); summation is for j = 1 to k, where k is the size of the composite (number of parental varieties of a particular composite) and n is the total number of parent varieties. Vj is the mean of varieties and Tj is the mean of topcrosses (pool of varieties as tester), and <IMG SRC="Image158.gif" WIDTH=16 HEIGHT=18>and <IMG SRC="Image159.gif" WIDTH=14 HEIGHT=17>are the respective average values in the whole set. Yield data from a 7 x 7 variety diallel cross were used for the variety means and for the "simulated" topcross means to illustrate the proposed procedure. The proposed prediction procedure was as effective as the prediction based on Yk = <IMG SRC="Image160.gif" WIDTH=16 HEIGHT=17>- (<IMG SRC="Image161.gif" WIDTH=16 HEIGHT=17> -<IMG SRC="Image156.gif" WIDTH=16 HEIGHT=18>)/k, where <IMG SRC="Image161.gif" WIDTH=16 HEIGHT=17>and <IMG SRC="Image158.gif" WIDTH=16 HEIGHT=18>refer to the mean of hybrids (F1) and parental varieties, respectively, in a variety diallel cross. It was also shown in the analysis of variance that the total sum of squares due to treatments (varieties and topcrosses) can be orthogonally partitioned following the reduced model Yjj’ = mu + ½(v j + v j’) + <IMG SRC="Image162.gif" WIDTH=12 HEIGHT=18>+ h j+ h j’, thus making possible an F test for varieties, average heterosis and variety heterosis. Least square estimates of these effects are also given

Prediction of Fatigue Strength Properties using Material Strength Database. 1st Report. Prediction of High-Cycle Fatigue Strength.

The dialogical integrated system for a material strength database (DIMS) was newly developed. In this study, the prediction procedures for fatigue strength properties were investigated using DIMS. It was found that the high-cycle fatigue strengths were accurately estimated by a multivariate analysis on the basic material factors of heat treated materials such as chemical composition and heat treatment temperature, under the limited conditions which were classified by materials used, heat treatment conditions and/or the fatigue fracture mode. From these results, the fatigue strength prediction procedure was proposed and examined for several published data sets, and it was found that the proposed prediction procedure, which used the multicorrelations among several material properties in the database system, was quite suitable for the prediction of high-cycle fatigue strength.