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In the foreseeable future, the traffic stream will be likely mixed with connected automated vehicles (CAVs) and regular vehicles (RVs). In the mixed traffic environment, when following a RV, due to the lack of vehicle-to-vehicle communications, it may take longer time for a CAV to sense and react than a human driver, which results in longer time headway and the loss of highway throughput. To address such a connectivity gap, this paper investigates an infrastructure-based solution, i.e., the deployment of roadside units to help CAVs in the heterogeneous traffic stream. Specifically, it is envisioned that these roadside units can sense vehicles in their coverage areas and provide the beyond-line-of-sight motion information to CAVs to empower them to react proactively, as they would do when following other CAVs. This paper is devoted to the analysis of the impacts of this type of roadside units at a strategic planning stage. In doing so, we first derive an analytical link performance function to capture their impact on the link capacity and travel time, and then develop a network equilibrium model to gauge their effect on travelers’ route choices and thus the flow distribution of both RVs and CAVs across the whole network. This modeling development will allow us to conduct a cost-benefit analysis for a given deployment plan of roadside units. For fair analyses, we further develop an optimization model to determine the optimal deployment plan for a given budget, while focusing on the worst case of its impact, because the flow distribution resulting from our network equilibrium model is not unique. Such a model provides a conservative estimate of the benefit brought by roadside units. Lastly, we offer case studies to demonstrate the models and unveil the potential of such an infrastructure-based solution.

The existing step size methods aim to success in the convergence of searching the minimum performance target point in the reliability analysis. However, these methods cannot guarantee that the performance function value is smaller than that of previous point. This results in the redundant computation during the search process. In this paper, a new step method called hybrid directional step method is proposed to improve the efficiency of search process. The proposed method can keep the performance function values of iterations presenting a decreasing sequence and quickly converge to the minimum performance target point. The proposed method is compared with some existing methods through several numerical examples. The results show that the proposed hybrid directional step method presents good robustness and more efficient computation in practical application.

The focus of this paper is on an evaluation of negative consequences due to the installation of pedestrian hybrid beacon (PHB) signals and to identify associated characteristics to proactively plan and reduce danger at mid-block locations. Data for thirteen mid-block locations with a PHB signal in the city of Charlotte, North Carolina was gathered to conduct before-after analysis using Empirical Bayes (EB) method. Data for fifty-nine mid-block locations without a PHB signal or similar treatment was considered as control locations for developing a safety performance function (SPF). The on-network characteristics were considered as the predictor variables, while the number of all crashes from 2011 to 2013 was considered as the dependent variable. The SPF was validated using 2014 data. A calibration factor was applied to estimate the number of all crashes at each PHB location. The results obtained from before-after EB analysis showed that the actual number of all crashes are relatively higher after the installation of PHB signals at five out of the thirteen PHB signal locations when compared with the estimated number of all crashes, had the PHB signal not been installed. Pearson correlation coefficients were estimated and used to examine the relationship between the number of all crashes and predictor variables such as demographic, socio-economic, land use, and on-network characteristics within the vicinity of each PHB signal location. The findings indicate that installing PHB signals at mid-block locations along high traffic volume, high-speed and wide roads, and, near office, multi-family, retail, and vertical mixed land use areas will lead to an increase in the number of all crashes after its installation.

A dynamic process model based on a base metal refinery including critical control layers has been developed. The critical control layers include sensors, actuators, regulatory controllers, alarm systems, safety interlocks and supervisory control. With the help of expert knowledge, a fault (abnormal event) library was incorporated into the dynamic model. Fault diagnosis methods are used to detect and identify abnormal process conditions. With the use of the dynamic process model, fault detection and identification methods can be more rigorously and accurately evaluated for hydrometallurgical industry use. An economic performance function is developed from expert knowledge. Once the fault diagnosis is complete, an economic impact analysis based on the fault diagnosis results is completed. A possible economic case for fault diagnosis is concluded from the results.

A new reliability analysis method for tunnels was developed by integrating an adaptive Radial Basis Function (RBF) metamodeling technique with a First-Order Reliability Method (FORM). In each iteration of the adaptive RBF (A-RBF) technique, an explicit metamodel of the performance function was constructed using an augmented RBF, before an alternate FORM was applied. Additional sample points were then added in the neighborhood of the design point in order to improve the accuracy of the metamodel. Three examples were solved and the proposed approach worked well. This work provides an efficient and effective reliability analysis method for practical tunnel engineering problems.

Accurate determination of the failure probability of oil and gas pipeline is very important in integrity assessment and work conditions of such structure. In this paper, the failure probability of the corroded pipelines which is made by X60 steel grade is evaluated. The burst corroded performance function is developed using the M5 model tree for this complex real engineering failure problem. The structural reliability of a the pressurized gas pipeline containing external corrosion defects has been evaluated using hybrid reliability method combined by the M5 model tree and Monte Carlo simulation. The results indicated that increasing the defects depth are strongly reduced the safety levels of this problem.

Under the condition of fuzzy inputs, the structure safety level is generally measured by the failure possibility. Differing from the level cut optimization method and the fuzzy simulation method, a novel method for estimating the failure possibility is proposed in this paper by combining the adaptive Kriging surrogate model with the Markov chain simulation. Based on the principle of fuzzy simulation, the fuzzy design point is defined as the failure point with the maximal joint membership function in the fuzzy simulation sample pool, and the problem of estimating the failure possibility can be transformed into that of searching the fuzzy design point. Then the Markov chain simulation which can quickly simulate the samples in the interest region is employed to search the fuzzy design point by a designed transfer criteria. To efficiently recognize the candidate states of the Markov chain, the Kriging surrogate model is adaptively trained in the candidate sample pool generated by the Markov chain states. By replacing the actual performance function with the convergent Kriging model, the efficiency of Markov chain simulation can be improved greatly. The presented examples are used to prove the rationality and high efficiency of the proposed method.

We show that the left-monotone martingale coupling is optimal for any given performance function satisfying the martingale version of the Spence–Mirrlees condition, without assuming additional structural conditions on the marginals. We also give a new interpretation of the left monotone coupling in terms of Skorokhod embedding which allows us to give a short proof of uniqueness.

The time-variant system reliability analysis is a significant topic in the field of reliability engineering. Although progress has been made, there remain multiple challenges in the existing methods including the explosive number of possible failure modes, requiring correlation information, and excessive computational efforts. In the present work, an improved moment method with high accuracy, efficiency and robustness is proposed for system reliability analysis of deteriorating structures. First, by introducing the complete system failure process, an equivalent time dependent performance function describing a deteriorating structural system is obtained. Second, a point estimate method based on the adaptive trivariate dimensional decomposition is adopted to calculate the first six moments of the performance function. And third, a saddlepoint approximation involving the first six moments is developed to estimate the system failure probability. Several examples are investigated to verify the accuracy, efficiency and stability of the proposed method.

Practically, the performance of many engineering problems can be defined using a complex implicit limit state function. Approximation of the accurate failure probability is very time-consuming and inefficient based on Monte Carlo simulation (MCS) for complex performance functions. M5 model tree (M5Tree) model is robust approach for simulation and prediction phenomena, which provides ability to dealing with complex implicit problems by dividing them into smaller problems. By improving the efficiency of reliability method using accurate approximated failure probability, an efficient reliability method using the MCS and M5Tree is proposed to calibrate the performance function and estimate the failure probability, respectively. The superiorities including simplicity and accuracy of M5Tree meta-model are investigated to evaluate the actual performance function through five nonlinear complex mathematical and structural reliability problems. The proposed reliability method-based MCS and M5Tree improved the computational efforts for evaluating the performance function in reliability analysis. The M5Tree significantly increased the efficiency of reliability analysis with accurate failure probability.

In this presentation, we propose to use the expertise gained in the cluster community in unbiased optimization to identify materials with optimal properties. Specifically, we present an inverse-design method that automatically identifies atoms or functional groups to be placed at pre-defined positions. Which groups are placed at which positions is identified automatically by optimizing a so called performance function. In the present work, we study, as a simple example, mixed Si-Ge clusters whereby their performance in solar-energy harvesting is optimized. We demonstrate that the proposed method is capable of identifying a larger number of very good candidate structures, although there are cases where not the best (but ‘only’ the 2nd best) structure is identified. It is also demonstrated that it can be difficult to predict the optimal system based on simple chemical and/or physical ideas. Finally, extensions and challenges of the present approach are being briefly discussed.

Probability of failure sensitivity analysis is a subject of major interest in uncertainty based optimization. However, the computational effort required to obtain accurate results is frequently very high. In this work we present a novel strategy for probability of failure sensitivity analysis, that is based on polynomial expansions of both the performance function and its derivatives. Sensitivity analysis is then made using the obtained polynomial expansions together with standard expressions. Since the simulation step requires only evaluation of closed form polynomials, very large samples can be used to obtain accurate results with small computational effort. The proposed approach is expected to be efficient when polynomial expansion methods are suitable. Four numerical examples are presented in order to show the effectiveness of the proposed approach.

Polynomial chaos expansion (PCE) is a versatile tool for building a meta-model in various engineering fields. Unfortunately, it is largely affected by the curse of dimensionality and its application for reliability analysis is usually hindered unless high truncated degrees are used. To alleviate this problem, this paper presents a new method based on contribution-degree analysis (CDA), an exact dimension-reduction model (DRM) and polynomial chaos expansion for efficient reliability analysis. First, the original performance function is decomposed as a summation of several component functions including one lower-dimensional interacting component function and several one-dimensional non-interacting component functions via a CDA-based DRM. Then, the full PCE or sparse PCE is employed to reconstruct the lower-dimensional interacting component function instead of the original complicated multi-dimensional performance function, whereas one-dimensional full PCEs are utilized for approximating the non-interacting component functions. In this way, we avoid building a PCE meta-model for the original multivariate performance function, thus the number of unknown coefficients is significantly reduced and the computational burden for reliability analysis is eased accordingly. Pertinent examples including both analytical performance functions and finite-element models are investigated, which demonstrates that the proposed method achieves a good trade-off of efficiency and accuracy for reliability analysis.

This paper presents a cost-effective probabilistic approach to be used in engineering applications. The proposed approach consists of an improved Kriging-based method aiming at reducing to a minimum the number of evaluations of the true performance function when computing a failure probability. It is a kind of variant of the classical Active learning method combining Kriging and Monte Carlo Simulation (AK-MCS) developed by Echard et al. (2011) [1], where some improvements are introduced to enhance the learning process. Some illustrative and practical examples are presented and discussed. The proposed approach has shown a great efficiency as compared to the classical AK-MCS approach.

In this study, we provide a theory to explain how bottom punishment or top reward enhances performance of teams when there is heterogeneity across players in cost–performance relationships. In contrast to the existing literature, we consider three types of performance functions: additive, weakest link (the performance is the min of performances of n contestants), and best shot (the performance is the max of performances of n contestants). For any of the three types, we derive easy-to-check sufficient conditions to judge whether reward or punishment is better. From the sufficient condition for the additive performance function, we know that punishment is better for less heterogeneous people and reward is better for more heterogeneous people. In addition, the sufficient conditions for the best-shot and weakest-link cases suggest that some unintuitive results hold; even under the best-shot (weakest link) performance function, the bottom punishment (top reward) becomes better when the gap in the abilities of contestants becomes very small (large).

A resilient system should have the ability to mitigate the disruption caused by unfavorable environment and to rapidly recover to an acceptable performance level. In this paper, a detailed model to assess resilience of shield tunnel is presented. The performance robustness under disruption and the subsequent recovery rapidity are emphasized in this model. The tunnel horizontal convergence is selected as the performance indicator. The resilience index (Re) is defined by the ratio of the integral of the performance transition function over the integral of the normal performance function. The rationality and applicability of the model is validated by a real case of extreme surcharge on Shanghai metro tunnel. In this case, the performance transition and the normal performance degradation are characterized by the measured data. 70–80% of the normal performance is disrupted due to the surcharge, but only 1% is recovered by unloading of the surcharge in 9 days and 12.4% is recovered after 4 years by the soil grouting in 38 days. It results in a resilience index (Re) between 0.28 and 0.45. The lesson learned from the case indicates that the high vulnerability of lining convergence due to the severe surcharge and the long time duration between recovery measures could result in weak resilient abilities for shield tunnels. The value of resilience index Re could be significantly increased by 73% on average if the recovery duration were shortened.

According to the National Highway Traffic Safety Administration's Fatality Analysis Report System, more than 25 percent of fatal crashes occur on horizontal curves. High Friction Surface Treatments are an innovative, cost-effective, and sustainable means of improving roadway safety, which could improve the safety of this alignment. This research involved a comprehensive review of crash data to identify the correlation between pavement skid number, roadway curvature degree, crash rate, and crash severity. The dataset showed that greater injury occurred on higher degrees of curvature, and lower skid numbers correlated to a higher percent of wet road crashes. Also, around a skid number of 55, an increase in skid number no longer resulted in decreased crash rates. The goal of this research is to use these correlations to develop Crash Modification Factor that would integrate pavement friction and horizontal curvature, to enhance the accuracy of the current Highway Safety annual performance function.

Civil infrastructure can be subjected to different deterioration scenarios (e.g., continuous deterioration and sudden events) during its service life. Time-dependent reliability analysis of a deteriorating system is of vital importance in the structural design, assessment, and management process. In this paper, a general and novel probability density function-informed framework for time-dependent reliability analysis is developed, considering different deterioration processes and system-level performance. Both continuous deterioration and sudden events are considered within the proposed framework. For the scenario with random sudden drops and/or significant differences in deterioration rates, a novel two-step translation method is proposed to assess the probability density of the performance function within the service life of a structure. In this method, translation operation is conducted twice; the first time to build a virtual and differentiable performance function, and the second, to solve the actual probability density function based on the results from the first step. Then, three illustrative examples are assessed: a simple deteriorating case, a component-level case, and a system-level case. The relevant results demonstrate the feasibility and efficiency of the proposed method.

Failure accidents of casings, designed obeying traditional design standards that have been amended for many times, are still happening nowadays, and existing reliability analysis methods mostly have not considered the dependence structures of variables, it's badly necessary to seek other methods to compensate these two problems and defects. This study proposes a copula-based risk analysis framework of casing in a casing-formation composite system (CFCS). Firstly, a mechanical analysis model of casing under CFCS is established and verified by the simulation method. Secondly, random data sets of the formation parameters are obtained by using logging data, meanwhile, the acquisition methods of statistical data of casing are also provided. Thirdly, the best uncertainty parameters in the given five copulas are inferred by using Bayesian-based Markov chain Monte Carlo (MCMC) likelihood algorithm, which are compared with those from other algorithms; the optimal copula is determined by the Bayesian-based MCMC-estimated method and the squared Euclidean distance (SED), respectively; the posterior joint probability isolines of the copulas are depicted, and isolines from the optimal copula are compared with that of the empirical copula method; the correlation coefficients of variables in CFCS, relative to performance function value, are sorted in magnitude. Finally, a division method of safety level is adopted, and based on the optimal copula, the risk analysis of casing is conducted with considering the bivariate dependence. The proposed framework can provide unique insights into the risk analysis of casing in CFCS with correlations considered and can also be programmed to use in risk management and decision-making systems of petroleum engineering.

Safety performance function (SPF) has been a vital tool in traffic safety evaluation including finding contributing factors to crashes, identifying hotspots, and assessing safety effects of countermeasures. In the United States (U.S.), the Highway Safety Manual provides a number of SPFs for a variety of road facilities. Due to the limited availability of traffic data in many regions, the transferability of SPFs has been an important topic in traffic safety analysis and has been evaluated by several studies. Nevertheless, the international transferability of freeway SPFs and the applicability of transferred SPFs on hotspot identification has been rarely investigated. Based on data from two Chinese cities, Shanghai and Suzhou, and three U.S. states, Texas, New York and Florida, this study analyzes the transferability of freeway SPFs between Chinese and U.S. regions. These SPFs are then transferred to the other country and their performance on hotspot identification is investigated. SPFs were developed in the frameworks of Poisson, Poisson-lognormal and negative binomial regressions for the five localities separately, and were calibrated using the calibration functions before being transferred. Without calibration, the poor model transferability was found between the two countries, while after calibration, the transferred SPFs between Shanghai/Suzhou and Texas/New York showed satisfactory performance on both model fitting and hotspot identification. However, the transferability of SPFs between Florida and the Chinese cities turned out to be unsatisfactory regardless of whether being calibrated or not, which was attributable to the considerable difference in traffic flow. The findings of this study are expected to be a good reference for researchers and practitioners who want to understand the transferability and applicability of SPFs in the international context.

In practice, there are many engineering problems characterized by complex implicit performance functions. Accurate reliability assessment for these problems usually requires very time-consuming computation, and sometimes it is unacceptable. In order to reduce the computational load, this paper proposes an efficient reliability method combining adaptive importance sampling and Kriging model based on the active learning mechanism. It inherits the superiorities of Kriging metamodel, adaptive importance sampling and active learning mechanism, and enables only evaluating the interested samples in actual performance function. The proposed method avoids a large number of time-consuming evaluation processes, and the important samples are mainly predicted by a well-constructed Kriging metamodel, thus the calculating efficiency is increased significantly. Several examples are given as validations, and results show that the proposed method has great advantages in the aspect of both efficiency and accuracy.

Behavior of reinforced concrete slabs with composite patches under centric punching load is presented in this paper. An experimental investigation is being performed. The parameters considered are: the ratio of patch size and the nature of the composite. Several reports are considered to account for the strengthened specific area. The dimensions of the patches are calculated so that the ratio patch X P /Y P is proportional to X D / Y D of the slab. Two types of composite CFRP and composite with cork fibers reinforcement are also considered in this study. The main objective is to evaluate the resistance of the concrete slabs reinforced by such composite patches and to define the parameters influencing the stiffness behavior. Using the Kinnunen and Nylander mechanism of rupture, an analytical model to predict the behavior of this type of tiles is proposed. An experimental investigation completed by theoretical modeling is carried out in order to assess the performances of the concrete slabs reinforced with composite patches under centric punching load. The average test and numerical results based on the finite element analysis in terms of overall and observed response are discussed and analyzed. The results show an improvement in terms of strength and mechanical performance function of the ratio of the dimensions of the composite patch which have a direct influence on the deformation before rupture.

Traditionally the literature on partnership has focused on understanding why firms choose to cooperate and with whom, however, our understanding of the impact of the resulting network structure on the performance of the project is limited. This study builds on joint RD in particular, the joint performance function in exploration projects is positively dependent on the number of partners; however, in exploitation projects this function depends positively on the density and intensity of interactions, and negatively on the number of partners. Our results complement previous research while adding empirical evidence on the nonlinear and contingent character of structural variables and the performance of joint projects.

To measure the effects of input variables’ realization on variance of the output performance function and on the failure probability of structural system, two new probabilistic importance measures (PIMs) are defined. As an input variable takes its realization according to its probability distribution, the two PIMs can quantify the possibility of reducing the variance of the output performance function and the possibility of improving the structural system reliability, respectively. After the properties of the PIMs are illuminated and proved in detail, a solution based on the probability density function evolution method (PDEM) is constructed to evaluate the PIMs. The solution is used to solve the PIMs with correlated input variables based on the Copula transformation. Examples demonstrate that the proposed solution on the PDEM can improve the computational efficiency greatly with acceptable precision, and the solution based on the conjunction of the PDEM and the Copula transformation can effectively solve the PIMs with correlated input variables.

An intuitive ellipsoidal perspective is described together with three spreadsheet-automated constrained optimizational FORM procedures and a SORM approach. The three FORM procedures are then compared in the context of geotechnical examples of a confined soil element, a rock slope, and an embankment on soft ground with spatially autocorrelated undrained shear strength in the soft clay foundation, the performance function of which is based on a reformulated Spencer method with search for reliability-based critical noncircular slip surface. Two methods of modeling spatial autocorrelations are presented, and the merits and limitations of the three constrained optimizational FORM procedures are studied. The complementary roles and interconnections among the three constrained optimizational FORM procedures and SORM approach are emphasized. Comparisons are also made with Monte Carlo simulations.

This work investigates the attitude tracking control problem of spacecraft under strong external disturbances and parameter uncertainties. A novel appointed-time stable control scheme is proposed with guaranteed transient and steady-state performance. First, an appointed-time reachable performance function (ARPF) is presented, and its reach time can be arbitrarily selected by the users. Then, a double-ARPFs strategy is introduced, that is, by imposing two ARPFs on the attitude and the system output, respectively, all system states will be appointed-time stable. Furthermore, a robust controller with implementable structure is proposed to guarantee the performance functions under strong external disturbances and parameter uncertainties. And the attitude tracking errors as well as the angular velocity errors are proved to be appointed-time stable. Last, three groups of simulations are organized to verify the effectiveness, robustness and appointed-time stability of the proposed control scheme.

The optimal operation scheduling of a pumping station with multiple pumps is formulated as a dynamic programming problem. Based on the characteristics of the problem, an extended reduced dynamic programming algorithm (RDPA) is proposed to solve the problem. Both the energy cost and the maintenance cost are considered in the performance function of the optimization problem. The extended RDPA can significantly reduce the computational time when it is compared to conventional DP algorithms. Simulation shows the feasibility of the reduction of the operation cost.

Reliability-based design optimization (RBDO) is a powerful tool for design optimization when considering the probabilistic characteristics of the design variables. However, its use in practical applications is hindered by the huge computational cost during the evaluation of structure reliability. A number of algorithms have been proposed, such as reliability index approach (RIA), performance measure approach (PMA), decoupled approach and single-loop approach. In this paper, the adaptive decoupling approach (ADA) is developed. It adopts a new update angle strategy and a novel feasibility-checking method to improve the efficiency of probabilistic optimization. The update angle is utilized to reduce the number of performance function evaluations. And the probabilistic constraints are divided into feasible, active and violated categories, and only violated and active probabilistic constraints are evaluated precisely. The computation capability of ADA is demonstrated using five examples which are widely used to test RBDO methods. The comparison results show that ADA has the similar accuracy as existing methods, and it is also very efficient.

An innovative framework for the performance assessment of a traditional water treatment plant (WTP) is presented that integrates the concepts of reliability, robustness, and Quantitative Microbial Risk Assessment (QMRA). Performance assessment for a WTP comprised of three units (i.e., unit 1: Coagulation/Flocculation and Sedimentation; unit 2: Filtration, and unit 3: Disinfection) was conducted. Performance functions for units 1, 2, and units 1 and 2 combined, were constructed by integrating turbidity robustness indices. Performance function for chlorine disinfection was developed based on the difference between achieved and required CT values. A health-based performance function was developed by comparing the target daily infection rate to the site-specific infection rate. It was used to identify whether the health-based target was met during the failures of units 1 to 3. Results obtained from the proposed performance functions can be used by operators to ensure that multiple barriers perform successfully under variable conditions.

A new switching mechanism for multiple model adaptive controllers (MMAC) is suggested in this paper. The proposed method gives superior performance in comparison to the widely used method of switching based on performance function and hysteresis function in systems which experience high levels of measurement noise. This method acts as a complementary condition within the switching mechanism which checks the existence of excitation in system at every instant. The new method is evaluated by simulation studies on a nonlinear model of a pH neutralization plant.

This study aims to find the difference between the slip surface having minimum factor of safety(called critical slip surface in deterministic analysis of slopes) and the slip surface having minimum reliability index(called critical reliability slip surface). The performance function used in reliability analysis was formulated by unbalanced thrust force method and the value of reliability index regarding given potential slip surface was obtained by Rosenbleuth method. The harmony search algorithm was adopted to alter the potential slip surface in order to locate the critical reliability slip surface. One complicated soil slope was analyzed and the results have shown that the critical slip surface is considerably different from critical reliability slip surface for multi-layer slopes and the reliability index of critical slip surface is larger than that of critical reliability slip surface.

This paper presents a model-based extremum seeking approach for a class of single-input-single-output nonlinear systems, with the analytic form of the performance function unknown a priori. We focus on a practically implementable design with robustness to model uncertainties and disturbances. A discrete-time sliding mode gradient estimator is developed for estimating the gradient of the performance profile. Based on the estimate, a variable structure output feedback regulator is proposed to enforce the system states toward the optimal trajectory. We analyze convergence conditions of the switching system toward a neighborhood of the optimal trajectory, and establish an ultimate bound on the size of the neighborhood. The robustness of the proposed controller is discussed with respect to measurement noise.

This paper examines the potential of relevance vector machine (RVM) for slope reliability by using first order second moment method (FOSM). The FOSM demands the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. The analysis of slope by limit equilibrium method gives implicit performance functions. Here, RVM has been used to predict implicit performance functions. RVM rely on the Bayesian concept and utilize an inductive modelling procedure that allows incorporation of prior knowledge in the estimation process. In this paper, an example is given regarding how the proposed RVM-based FOSM analysis can be carried out. This study shows that the proposed RVM-based FOSM is viable alternative for slope reliability analysis.

Objective The purpose of this research was to investigate patterns of observational learning implemented by team sport athletes, coaches, and officials. Method The Functions of Observational Learning Questionnaire (FOLQ; Cumming, Clark, Ste-Marie, McCullagh, & Hall, 2005 ) was administered to 240 participants (20 athletes, coaches, and officials from basketball, baseball, ice hockey and soccer respectively). Results Analysis of variance revealed that the skill function (M = 5.33, SD = 1.16) was significantly highest across all participants, followed by the strategy (M = 4.81, SD = 1.23), then performance functions (M = 3.33, SD = 1.23). The results also indicated that coaches (M = 5.62, SD = 0.90) scored significantly higher than athletes (M = 5.16, SD = 1.27) and officials (M = 5.20, SD = 1.23) on the skill function; coaches (M = 5.15, SD = 1.13) ranked significantly higher than athletes (M = 4.74, SD = 1.32) and officials (M = 4.55, SD = 1.15) on the strategy function; and officials (M = 3.61, SD = 1.20) scored significantly higher than coaches (M = 3.05, SD = 1.23) on the performance function. Conclusions The pattern of observational learning for all participants was similar to previous findings. Individually, coaches scored highest on the skill and strategy functions, while officials scored highest on the performance function. The results are noteworthy as they provide the first comparison of athletes, coaches, and officials. Comparisons amongst these sport participants may lead to a better understanding of the learning process that occurs through observation for athletes, coaches, and officials.

This paper deals with the reliability analysis of slope stability with spatial variability of soil properties. Supposing an elastic soil behavior until yield criteria, a stresses mobilized along slip surface are evaluated to formulate the performance function. The results of SFEM (Stochastic Finite Element Method) and the limit equilibrium method, such as Bishops simplified method are analyzed to check their efficiencies and accuracies. To look for critical slip surface, an optimization strategy is performed, also a sensitivity studies are carried out in order to investigate the influence of random field parameters used to model the spatial variability of soil properties.

Objectives The purposes of this study were to investigate adult sport novices' use of the functions of observational learning and to examine its relationship to their self-efficacy beliefs to learn sport-related skills and strategies, and to regulate mental states during the learning process. Method Adults enrolled in beginner level sport classes completed the Functions of Observational Learning Questionnaire (FOLQ; Cumming, J., Clark, S.E., Ste-Marie, D.M., McCullagh, P., & Hall, C. (2005). The functions of observational learning questionnaire (FOLQ). Psychology of Sport and Exercise, 6 , 517–537.) as well as a self-efficacy questionnaire. Internal consistencies were acceptable for all subscales and a factor analysis confirmed that this instrument can be used with sport novices. Results Athletes' use of observational learning and their self-efficacy beliefs differed according to sport type. Hierarchical multiple regression analyses revealed that for adults learning an independent sport, more frequent use of the skill function of observational learning predicted higher self-efficacy to learn skills and self-efficacy to learn strategies. For adults learning an interactive sport, more frequent use of the performance function predicted higher self-efficacy to regulate mental states during the learning process. Conclusions Results suggest that factors related to specific sport types, such as sport demands and model availability, may differentially influence learners' use of observational learning as well as its impact on their self-efficacy for learning technical sport components and self-efficacy for controlling their mental state during learning. This has implications for sport instructors and coaches regarding optimal methods for structuring observational learning experiences to enhance learners' self-efficacy beliefs.

In this paper, we consider state feedback robust control problems for discrete-time nonlinear systems subject to disturbances. The objective of the control is to minimize a performance function while guaranteeing a prescribed quantitative input to state stability (ISS) property for the closed-loop systems. By introducing the concept of ISS control invariant set, a sufficient condition for the problem to be feasible is given. Built on the sufficient condition, a computationally efficient control design algorithm based on one-step min-max optimization is developed. An example is given to illustrate the proposed strategy.

The 921 Chi-Chi earthquake (Mw=7.6) in 1999 caused extensive ground failures in the central part of Taiwan. Severe damages due to soil liquefaction and ground softening occurred widespread in Wufeng. Various phenomena such as sand boils, settlement, horizontal translation of a building, and lateral spreading associated with liquefaction and ground softening were observed in Wufeng and documented. The locations of soil liquefaction, settlement, and no observed ground failures in Wufeng were also identified, investigated, and analyzed after the earthquake. The areas of sand boils and lateral spreading are mainly located beside the creeks and match the regions of present and old river channels pretty well. In situ soil profiles and material properties of Wufeng are obtained based on 25 borings and six cone penetrometer (CPT) soundings before and after the earthquake. Results of aftershocks and data obtained from Nantou Township are compiled in the analyses to delineate the boundary of liquefaction and nonliquefaction. Data of standard penetration tests (SPT), CPT, and shear wave velocity are selected and the empirical methods recommended by an NCEER workshop were employed to evaluate the liquefaction resistance of soils. The results are also compared with the performance functions obtained through artificial neural network modeling. The SPT-based performance function has better prediction on the cases of nonliquefaction than other empirical methods do. Impacts of fines content (FC) (both nonplastic and plastic) on liquefaction resistance are also found to be profound. For SPT cases, an empirical boundary is suggested for soils with fines content larger than 35%.

A few comments on moment method for reliability analysis advocated by Zhao and Ono are presented in the paper. It is demonstrated using a number of numerical examples that the first four moments of a distribution, particularly when they are crudely estimated, do not always allow good estimates of tail probabilities, and that some different equivalent forms of performance function that describe the same structural problem may give totally incorrect values for the reliability when the Zhao and Ono methodology is applied to them. Furthermore, the computation method for the first four moments proposed by the same authors may be incorrect so that the reliability result is unreasonable accordingly.

During the last quarter of a century, the evolution of the thermal sensitivity of performance in ectotherms has become a major focus of research programs in evolutionary physiology. Graphical and mathematical models describe how the relationship between body temperature and performance, termed the performance function, should evolve in response to the thermal environment. Interspecific comparisons of the thermal sensitivity of locomotor performance have revealed that the performance function is evolutionarily labile in some taxa but is more conservative in others. A lack of heritable variation or weak selection on performance may explain the conservation of thermal physiology in certain groups, but evolutionary trade-offs do not appear to have been important constraints. Other aspects of thermal physiology, such as the thermal sensitivity of growth rate, have evolved rapidly in ectotherms. Despite the apparent lability of thermal physiology in some taxa, there is limited evidence that thermoregulatory behavior and thermal physiology are coadapted. Future studies should broaden taxonomic and phenotypic foci, while paying close attention to the assumptions of current theories.

Traditionally the mechanical and control design of robots are performed separately and based on different objectives. This paper provides a framework for the integrated/concurrent mechanical and control design of flexible macro/micro (M/m) robots. Nonlinear programming methods arc used for generating the optimal values of mechanical and control design variables. To illustrate the Integrated Design Method (IDM), and to establish its superiority over the Traditional Design Method (TDM), three design case studies are performed. The design constraints are provided, and a new performance measure is proposed for minimizing the tracking error of a flexible M/m robot.

This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating and comparing the performance of spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviours, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different taks by normalizing for task complexity. After presenting PARADISE, we illustrate its application to two different spoken dialogue agents. We show how to derive a performance function for each agent and how to generalize results across agents. We then show that once such a performance function has been derived, it can be used both for making predictions about future versions of an agent, and as feedback to the agent so that the agent can learn to optimize its behaviour based on its experiences with users over time.

An improved neural network inverse control method, which combines the idea of predictive control with neural network inverse control, is presented. It uses multi-step predictive performance function to train the weights of neural network controller. The new method shows higher dynamic speed and greater accuracy than general neural network inverse control. The novel method is applied to the outlet temperature control of the electric heating furnace in a small hydrogenation test equipment, and the promising control results are obtained.

This paper utilizes the intervening design variables concept for structural reliability analysis. This procedure was traditionally used in structural optimization, whereas this work applies to problems having stochastic information. The present algorithm is on the basis of a safety index algorithm present in Ref. [1] [L. P. Wang and R. V. Grandhi, Efficient safety index calculation for structural reliability analysis. Comput. Struct. 52, 103–111 (1994)], which utilized the nonlinear approximation of performance function in the original space of random variables. The proposed algorithm further develops this procedure by (i) implementing the adaptive nonlinear approximation of performance function in the standard normal space of random variables, (ii) using an improved intervening variable procedure for more accurate approximation, and (iii) adding an additional convergency check on safety index calculation using approximate gradients. The efficiency and robustness of the proposed algorithm are demonstrated by several examples with highly nonlinear, complex, and explicit/implicit performance functions.

This paper discusses the convexifying approach to separable nonconvex large-scale steady-state systems with general constraints by means of partly introducing the penalty terms to the constraints. A new approach has been suggested which not only convexifies the original nonconvex performance function, but also capable of making the augmented Lagrangian function separable. The hierarchical optimization algorithm used to solve the large-scale problem is given, and the convergence proof as well as the estimation of the rate of convergence are also discussed. Simulation of a two subsystems example is also given.

A method for estimating the value of the eddy viscosity coefficient of water flow is presented for the analysis of tidal current. The main features of the present method are described as follows: (1) The shallow water equation considering the viscosity term is employed as the numerical model. (2) The tidal current is modeled by assimilation of periodic flow with several constituents. (3) The performance function is assumed to be a norm of the difference between the observed and computed water elevation at the specified points. (4) The Fletcher-Reeves method, a conjugate gradient method, is applied to minimize the performance function. Two numerical tests based on the simple flow model are carried out to verify the applicability and properties of the method. The value of the eddy viscosity coefficient in Tokyo Bay is estimated by the present method. It is seen that the results of the method are much affected by zoning depending on the water depth.

In this note we consider the non-cooperative linear feedback Nash quadratic differential game with an infinite planning horizon for descriptor systems of index one. The performance function is assumed to be indefinite. We derive both necessary and sufficient conditions under which this game has a Nash equilibrium.

In this note, we consider the non-cooperative linear feedback Nash quadratic differential game with an infinite planning horizon. The performance function is assumed to be indefinite and the underlying system affine. We derive both necessary and sufficient conditions under which this game has a Nash equilibrium. As a special case, we derive existence conditions for the multi-player zero-sum game.

A new method for yield optimization is presented that considers the high computational costs for circuit simulations and the typical parameter partitioning of integrated device models in deterministic and statistical parameters. It is based on a systematic concept for performance function macromodeling, that allows a flexible trade-off between simulation costs and modeling accuracy. Using quadratic performance models, an analytical expression for the yield gradient is derived by analyzing the performance statistics. Based on this yield gradient, an efficient deterministic optimization procedure will be proposed, which maintains the individual view of performance properties.