Reliability analysis of structure with random parameters based on multivariate power polynomial expansion

A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, structuralgeometric characteristics and static loads. The structuralresponse is first expressed as a multivariable power polynomialexpansion, of which the coefficients ae then determined by utilizing the higher-order perturbation technique and Galerkinprojection scheme. Then, the final performance function ofthe structure is determined. Due to the explicitness of theperformance function, a multifold integral of the structuralfailure probability can be calculated directly by the Monte Carlo simulation, which only requires a smal amount ofcomputation time. Two numerical examples ae presented toillustate te accuracy ad efficiency of te proposed metiod. It is shown that compaed with the widely used first-orderreliability method （ FORM） and second-order reliabilitymethod （ SORM）, te results of the proposed method are closer to that of the direct Monte Carlo metiod,and it requires much less computational time.

Shrinkage Estimation in the Random Parameters Logit Model

In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.

Modelling lane-changing execution behaviour in a connected environment:A grouped random parameters with heterogeneity-in-means approach

Lane-changing is performed either to follow the route to a planned destination(i.e.,mandatory lane-changing)or to achieve better driving conditions(i.e.,discretionary lane-changing).A connected environment is expected to assist during lane-changing manoeuvres,but it is not known well how driving aids in a connected environment assist lane-changing execution.As such,this study investigates the impact of a connected environment on lanechanging execution time during mandatory and discretionary lane-changing manoeuvres.To this end,this study designed an advanced driving simulator experiment where 78 drivers performed these manoeuvres on a simulated motorway in three randomised driving conditions.The conditions were baseline(without driving aids),a fully functioning connected environment with a perfect supply of driving aids,and an impaired connected environment with delayed driving aids.The lane-changing execution time has been modelled by a random parameters hazard-based duration modelling approach,which accounts for the panel nature of data and captures the unobserved heterogeneity.Results suggest that,compared to the baseline condition(i.e.,a non-connected environment),most of the drivers in the connected environment take more time to complete their lane-changing manoeuvres,indicating drivers’safer lane-changing execution behaviour in the connected environment.The communication delay driving condition has been found to have more deteriorating effects on mandatory lanechanging manoeuvres than discretionary lane-changing manoeuvres.This study concludes that(i)the connected environment increases safety margin during both lane-changing manoeuvres,and(ii)a higher magnitude of safety margin is observed during mandatory lane-changing manoeuvres whereby drivers have a higher need for assistance.

Modeling severities of motorcycle crashes using random parameters

One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features,traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries.Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally,the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.

Evaluating the impact of traffic violations on crash injury severity on Wyoming interstates:An investigation with a random parameters model with heterogeneity in means approach

This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alternative to the mixed logit model.This was done to better account for unobserved heterogeneity in the crash data.As per the results,the random parameters model with heterogeneity in means not only exhibited a better fit but also uncovered more insights regarding the factors influencing crash injury severity.The advanced model showed that traffic violations,crash characteristics and environmental characteristics among other factors impact crash injury severity on Wyoming’s interstate highways.With regards to traffic violations,driving too fast for prevailing conditions and driving under the influence of alcohol and drugs were identified as the main violations that significantly influenced crash severity.Among other useful insights,the heterogeneity in mean specification indicated that the likelihood of severe injury crashes is increased by the interactive effect between non-trucks(vehicles not classified as trucks)and driving too fast for conditions.This is a significant implication that high speed behavior by non-truck drivers in adverse weather conditions is ranked as one of the hazardous traffic violations on Wyoming’s interstates.This study provided for the first time important information on the impact of traffic violations on crash severity of crashes that occurred on challenging roadways that characterized by mountainous terrain and severe weather conditions.Results from the study will help enforcement agencies in the state to better identify appropriate countermeasures to mitigate the impact of violations on crash severity.

Dynamic simulation of beam-like structure with a crack subjected to a random moving mass oscillator

In this paper, dynamic simulation of a beam-like structure with a transverse open crack subjected to a random moving mass oscillator is investigated. The simultaneous effect of a crack and a random oscillator has not been addressed up to now. The crack in the beam at different locations and with different depths is considered as one group of damage, each as an individual imperfection. In addition, bearing immobility is considered as another type of problem in the beam. Mass, stiffness, damping and velocity of the oscillator are assumed to be random parameters. An improved perturbation technique is applied to reduce the simulation time. It was found that there is a maximum value of the variance of each uncertain parameter, in which the maximum reliability of the perturbation method can be achieved, and that this maximum value can be obtained by the Alpha-Hilber Monte-Carlo simulation method. The simulation results reveal that the mass and the velocity uncertainty cause high uncertainty in the deflection of the beam. Also, the pattern of the deflection is not affected by different random oscillator parameters, and as a result, the type of damage can be identified even with high uncertainty. Moreover, the deflection in the nodes around the mid-span of the beam provides the best information regarding the imperfections, and consequently leads to the best sensor locations in an actual experiment.

Analysis of bicyclist-vehicle crash at intersection area considering behavior prior to crash:A random parameter ordinal probit approach

In order to analyze the risky factors that affect vehicle-cyclist crash injury severity at the intersection area,especially the factors relating to the road users behaviors,an empirical study was conducted by collecting accident records from 2011 to 2015 from the General Estimates System.After preliminary screening,the variables were classified into 5 main categories including cyclists characteristic and behavior,drivers characteristic and behavior,vehicle characteristic,intersection condition,and time.The random parameter ordinal probit(RPOP)was used to study the significant influencing factors and corresponding heterogeneity.The results show that failing to obey traffic signals,failing to yield to right-of-way,dash and drinking before cycling can increase the injury severity for cyclists,and the corresponding fatal injury likelihoods increase by 53.2%,40.0%,86.3%,and 211.5%,respectively.Moreover,drivers inattention,speeding,going straight and left turning increase the risk of crashing for cyclists.The corresponding fatal injury likelihoods increase by 134.5%,186.5%,69.3%,and 22.7%,respectively.Other indicators such as age,gender,vehicle type,traffic signal and intersection type can also affect injury severity.

Stochastic period-doubling bifurcation analysis of a Rssler system with a bounded random parameter

This paper aims to study the stochastic period-doubling bifurcation of the three-dimensional Rossler system with an arch-like bounded random parameter. First, we transform the stochastic RSssler system into its equivalent deterministic one in the sense of minimal residual error by the Chebyshev polynomial approximation method. Then, we explore the dynamical behaviour of the stochastic RSssler system through its equivalent deterministic system by numerical simulations. The numerical results show that some stochastic period-doubling bifurcation, akin to the conventional one in the deterministic case, may also appear in the stochastic Rossler system. In addition, we also examine the influence of the random parameter intensity on bifurcation phenomena in the stochastic Rossler system.

Stochastic period-doubling bifurcation in biharmonic driven Duffing system with random parameter

Stochastic period-doubling bifurcation is explored in a forced Duffing system with a bounded random parameter as an additional weak harmonic perturbation added to the system. Firstly, the biharmonic driven Duffing system with a random parameter is reduced to its equivalent deterministic one, and then the responses of the stochastic system can be obtained by available effective numerical methods. Finally, numerical simulations show that the phase of the additional weak harmonic perturbation has great influence on the stochastic period-doubling bifurcation in the biharmonic driven Duffing system. It is emphasized that, different from the deterministic biharmonic driven Duffing system, the intensity of random parameter in the Duffing system can also be taken as a bifurcation parameter, which can lead to the stochastic period-doubling bifurcations.

Hopf bifurcation of nonlinear system with multisource stochastic factors

The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposi-tion method and the Karhunen-Loeve(K-L)decomposition theory.Secondly,the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained.At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored.Finally,the theorical results are verified by the numerical simulations.

Stochastic period-doubling bifurcation analysis of stochastic Bonhoeffer-van der Pol system

In this paper, the Chebyshev polynomial approximation is applied to the problem of stochastic period-doubling bifurcation of a stochastic Bonhoeffer-van der Pol （BVP for short） system with a bounded random parameter. In the analysis, the stochastic BVP system is transformed by the Chebyshev polynomial approximation into an equivalent deterministic system, whose response can be readily obtained by conventional numerical methods. In this way we have explored plenty of stochastic period-doubling bifurcation phenomena of the stochastic BVP system. The numerical simulations show that the behaviour of the stochastic period-doubling bifurcation in the stochastic BVP system is by and large similar to that in the deterministic mean-parameter BVP system, but there are still some featured differences between them. For example, in the stochastic dynamic system the period-doubling bifurcation point diffuses into a critical interval and the location of the critical interval shifts with the variation of intensity of the random parameter. The obtained results show that Chebyshev polynomial approximation is an effective approach to dynamical problems in some typical nonlinear systems with a bounded random parameter of an arch-like probability density function.

Households’Willingness-to-Pay for Wastewater Treatment in Traditional Agro-Food Processing Villages,Nhue-Day River Basin,Vietnam:Case Study in Hanoi City

Despite the number of studies focusing on the financial analysis of production activities, conducting on technical solutions, and improving water quality, no study has been conducted on the application of economic instruments that apply to water quality management in craft villages, and several studies of WTP also. This study aimed to estimate the households’ willingness-to-pay for wastewater treatment in selected traditional agro-food processing villages in Nhue-Day River Basin, Vietnam. A pilot Choice Experiment (CE) technique in Choice Modelling (CM) approach was applied for this study with 267 selected agro-food processing households by using the conditional logit (CL) and random parameter logit (RPL) models. The results showed that total annual environmental fee for wastewater treatment from agro-food processing households is estimated as 1089 million VND (equal to US$47,868 per year) for the total of 902 agro-food processing households in three research sites in Nhue-Day River Basin. This estimated budget for wastewater treatment accounted for 55.85% of total annual operation and maintenance costs only. In addition, the technology is improved to enable 90% of treated wastewater. Overall, the results of this study suggest the new wastewater treatment plant construction and improved wastewater collection system by increasing the investment in order to improve the water quality in Nhue-Day River Basin that brings about the reducing environmental degradation, biodiversity loss and human health risks.

QUANTITATIVE EVALUATION OF ARRANGEMENT OF MONOMERS IN LINEAR BINARY COPOLYMERS USING A MONTE CARLO SIMULATION METHOD

A Monte Carlo method was used to take thorough account of the influences of different reactivity ratios and initial feed compositions on copolymer microstructure.The model proves the lack of azeotropic behavior in systems in which r_A>1 and r_B<1 or vice versa;it is also able to calculate the drift in the copolymer properties:copolymer composition,and randomness parameter.Moreover,for each reactivity ratio pair given,there is a unique reaction conversion,at which macromolecules produced inherit their ...

Investigating safety and liability of autonomous vehicles:Bayesian random parameter ordered probit model analysis

Purpose–This study aims to investigate the safety and liability of autonomous vehicles(AVs),and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.Design/methodology/approach–The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations.The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs,respectively,as well as accommodating the heterogeneity issue simultaneously.Findings–The findings show that day,location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.Originality/value–The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Effect of Randomness of Interfacial Properties on Fracture Behavior of Concrete Under Uniaxial Tension

Interfacial transition zones （ITZs） between aggregates and mortar are the weakest parts in concrete. The random aggregate generation and packing algorithm was utilized to create a two-phase concrete model, and the zero-thickness cohesive elements with different normal distribution parameters were used to model the ITZs with random mechanical properties. A number of uniaxial tension-induced fracture simulations were carried out, and the effects of the random parameters on the fracture behavior of concrete were statistically analyzed. The results show that, different from the dissipated fracture energy, the peak load of concrete does not always obey a normal distribution, when the elastic stiffness, tensile strength, or fracture energy of ITZs is normally distributed. The tensile strength of the ITZs has a significant effect on the fracture behavior of concrete, and its large standard deviation leads to obvious diversity of the fracture path in both location and shape.

Exploring traffic safety climate with driving condition and driving behaviour:a random parameter structural equation model approach

This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.

Exploring the stimulative effect on following drivers in a consecutive lane change using microscopic vehicle trajectory data

Improper lane-changing behaviours may result in breakdown of traffic flow and the occurrence of various types of collisions.This study investigates lane-changing behaviours of multiple vehicles and the stimulative effect on following drivers in a consecutive lanechanging scenario.The microscopic trajectory data from the HighD dataset are used for driving behaviour analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario,and not only distance-and speed-related factors but also driving behaviours are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver’s psychological heterogeneity in the consecutive lane-changing situation.Furthermore,a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision.Results indicate that 1)the consecutive lane-changing behaviours have a significant negative effect on the following lane-changing vehicles after lane change;2)the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers;and 3)the utility prediction model can be used to detect an improper lane-changing decision.

A direct-variance-analysis method for generalized stochastic eigenvalue problem based on matrix perturbation theory

It has been extensively recognized that the engineering structures are becoming increasingly precise and complex,which makes the requirements of design and analysis more and more rigorous.Therefore the uncertainty effects are indispensable during the process of product development.Besides,iterative calculations,which are usually unaffordable in calculative efforts,are unavoidable if we want to achieve the best design.Taking uncertainty effects into consideration,matrix perturbation methodpermits quick sensitivity analysis and structural dynamic re-analysis,it can also overcome the difficulties in computational costs.Owing to the situations above,matrix perturbation method has been investigated by researchers worldwide recently.However,in the existing matrix perturbation methods,correlation coefficient matrix of random structural parameters,which is barely achievable in engineering practice,has to be given or to be assumed during the computational process.This has become the bottleneck of application for matrix perturbation method.In this paper,we aim to develop an executable approach,which contributes to the application of matrix perturbation method.In the present research,the first-order perturbation of structural vibration eigenvalues and eigenvectors is derived on the basis of the matrix perturbation theory when structural parameters such as stiffness and mass have changed.Combining the first-order perturbation of structural vibration eigenvalues and eigenvectors with the probability theory,the variance of structural random eigenvalue is derived from the perturbation of stiffness matrix,the perturbation of mass matrix and the eigenvector of baseline-structure directly.Hence the Direct-VarianceAnalysis(DVA)method is developed to assess the variation range of the structural random eigenvalues without correlation coefficient matrix being involved.The feasibility of the DVA method is verified with two numerical examples(one is trusssystem and the other is wing structure of MA700 commercial aircraft),in which the DVA method also shows superiority in computational efficiency when compared to the Monte-Carlo method.

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

With the increased frequency of extreme weather events and large-scale disasters, extensive societal and economic losses incur every year due to damage of infrastructure and private properties, business disruptions,fatalities, homelessness, and severe health-related issues. In this article, we analyze the economic and disaster data from1970 through 2010 to investigate the impact of disasters on country/region-level economic growth. We leveraged a random parameter modeling approach to develop the growth-econometrics model that identifies risk factors significantly influencing the country/region-level economic growth in the face of natural hazard-induced disasters,while controlling for country/region-and time-specific unobserved heterogeneities. We found that disaster intensity in terms of fatalities and homelessness, and economic characteristics such as openness to trade and a government's consumption share of purchasing power parity(PPP), are the significant risk factors that randomly vary for different countries/regions. Other significant factors found to be significant include population, real gross domestic product(GDP), and investment share of PPP converted GDP per capita. We also found that flood is the most devastating disaster to affect country/region-level economic growth. This growth-econometrics model will help in the policy and decision making of governmentsrelated to the investment needs for pre-and post-disaster risk mitigation and response planning strategies, to better protect nations and minimize disaster-induced economic impacts.

Injury severities from heavy vehicle accidents:An exploratory empirical analysis

Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.