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Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.

The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.

The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.

The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

When dealing with simple functional functions, traditional reliability calculation methods, such as the linear second-order moment and quadratic second ordered moment, Monte Carlo simulation method, are powerful. However, when the functional function of the structure shows strong nonlinearity or even implicit, traditional methods often fail to meet the actual needs of engineering in terms of calculation accuracy or efficiency.

To improve the reliability analysis efficiency and calculation accuracy of complex structures, the reliability analysis methods based on parametric and semi-parametric models are analyzed.

This paper proposes a reliability method that combines the Kriging model and the importance sampling method to improve the calculation efficiency of traditional reliability analysis methods.

This method uses an active learning function and introduces an importance sampling method to screen sample points and shift the center of gravity, thereby reducing the sample size and the amount of calculation.

To provide valuable information for scholars to grasp the current situations, hotspots and future development trends of reliability analysis area.

In this paper, recent researches on efficient reliability analysis and applications in complex engineering structures like aeroengine rotor systems are reviewd.

The recent reliability analysis advances of engineering application in aeroengine rotor system are highlighted, it is worth pointing out that the surrogate model methods hold great efficiency and accuracy advantages in the complex reliability analysis of aeroengine rotor system, since its strong computing power can effectively reduce the analysis time consumption and accelerate the development procedures of aeroengine. Moreover, considering the multi-objective, multi-disciplinary, high-dimensionality and time-varying problems are the common problems in various complex engineering fields, the surrogate model methods and its developed methods also have broad application prospects in the future.

For the strong demand for efficient reliability design technique, this review paper may help to highlights the benefits of reliability analysis methods not only in academia but also in practical engineering application like aeroengine rotor system.

In the present study, the authors examined whether academic performance [grade point average (GPA)] can be predicted by self-reported frequency of memorization and recitation, verbatim memory performance, and self-efficacy in a sample of college students from Saudi Arabia.

Students' verse memory, word memory, experience with memorization and recitation, as well as general self-efficacy were measured. GPA was provided by the Office of the Registrar.

Verbatim memory performance for individual words and verses moderately predicted GPA.

To be determined is the extent to which memory skills for different materials are related to memorization and recitation practice as well as encoding preferences.

The findings indicate that even though in college a premium is placed on activities that transform the format of the materials to be learned, activities that replicate materials may still be helpful.

In Western pedagogy, memorization and recitation are considered counterproductive modes of information acquisition. The findings of this study illustrate that retention is an essential processing step upon which the complex cognitive activities that are embedded in college-level curricula rely.

The extant literature illustrates the benefits of exceptional memorization and recitation training. The findings suggest that academic success is positively related to what would be judged as moderate practice, thereby supporting the notion that benefits exist.

Many studies document the importance of learner-centered active teaching to improve college students' critical engagement with challenging problems presented by our information-rich twenty-first-century environment. Others indicate that students from less privileged backgrounds often struggle even in well-designed classrooms. What is lacking is a mechanism for understanding these divergent outcomes and designing courses that better meet the needs of the diverse students in the college classroom. In this chapter, an argument is presented for understanding college student learning and curriculum design through the lenses of epistemological development and behaviors of learning. The consensus model presents descriptions of four epistemological stages, creating a framework to help classroom practitioners and administrators better understand the abilities of their students. The foundational assumption is that using appropriate curricular components will support student engagement and epistemological and self-regulation growth. To support this assumption, the model is accompanied by research-supported activities and strategies that benefit learners at different developmental stages and with different degrees of self-regulation. Moreover, intentional and reflective teaching has the potential to improve faculty understanding about the nature of learning and acceptance of learner-centered pedagogies, which will also have positive consequences for students. The end result will be a more inclusive learning environment with improved outcomes for a wider range of students.

Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.

In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.

The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.

This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.

Academic libraries have long been central to the campus ecosystem. From one-room collections housed in multi-functional buildings of the colonial college campus to the modern-day cathedrals where collections, patrons, and technologies collide, academic libraries have been a steadfast, yet flexible pillar of the higher education system. Employing a case study approach, this chapter reveals how one institution, the Ohio University Libraries (OUL), has reimagined the use of library space in response to twenty-first-century demands.

A visioning process undertaken by OUL culminated in a master plan intended to serve as a guide to space utilization and renovation strategies for nearly every floor of the seven-story facility. Beyond the master planning process, external demand for space within the library emerged organically. Given these two realities, OUL’s actions over the last decade have been guided by two main approaches to the use and redesign of space: (1) repurposing space for library-oriented initiatives and (2) co-locating complementary student support services within the library. Collectively, the examples highlighted in this chapter reveal how OUL has redesigned library space and continues to be an innovative environment in response to changing demands.

Society may be on the verge of a revolutionary phase of mobile device use in higher education generally and in libraries in particular. This paper seeks to address this issue.

Through an examination of trends and technological developments in the area of mobile devices and a review of the potential of mobile devices, the paper analyzes the potential of mobile devices in academic libraries.

Most college students own cell phones and laptops and the capabilities of these and other devices are expanding.

Libraries have the opportunity to extend new types of services to users of mobile devices and to develop, license, or otherwise make available scholarly content that is configured for mobile devices. Ideally, libraries will become part of an institutional planning process for the development of services for mobile devices.

The more pervasive use by students of smartphones, the uptake of e‐book readers, and the increasing use of mobile devices in some areas of the curriculum all have implications for libraries.

Some writers in this area believe that the increased capabilities of mobile devices could lead to new forms of engagement with student learning; this possibility can be embraced by academic libraries that seek to be strong partners in the teaching and learning process of their institution.

The paper synthesizes developments and provides suggestions for the future.

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty.

In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process.

Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties.

A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.