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AbstractIn customer-driven design of systems or products, one has performance targets in mind and would like to determine values for product or system parameters that meet such targets. Engineering computer simulation models predict performance given design parameter values; meeting a target is done iteratively through an optimization search procedure, typically by optimizing a regression, neural network or other type of approximation metamodel of the computer model. We pose the thesis that, since forward metamodel construction is a key part of this strategy, constructing an inverse metamodel directly is advantageous. The inverse metamodel obviates the need for optimization in many settings. One can design experiments that allow simultaneous fitting of forward and inverse metamodels. We discuss the potential for this strategy, the connection with the calibration problem, and some of the issues that must be resolved to make the approach practical. [ABSTRACT FROM AUTHOR]

Data-centric near-real-time intelligent process control for smart manufacturing in an Industry 4.0 era is of tremendous value. Design and manufacturing of high-performance ductile iron sand castings is a multi-variant complex process with much uncertainty involved. As a result, in spite of a well-controlled operation and an experienced workforce, iron foundries in a production environment do face sporadic shrinkage and lots with nonconforming property requirements, resulting in scrap or rework. A framework and methodology consisting of AI (artificial intelligence) and ML (machine learning) tools, coupled with ICME (integrated computational materials engineering) and process simulation tools, will be presented to quantify uncertainty (UQ). Metamodels, both predictive and prescriptive in near real time were developed using such AI/ML techniques using historical production and selective design of experiments (DOE)-generated additional data. The data will be presented including details on successful corrective action production trials. The proposed framework and approach is applicable to solve such complex problems encountered in the foundry and machining operations where there is uncertainty. [ABSTRACT FROM AUTHOR]

The structural similarity of biological, organic and inorganic substances in the nanoworld, on the one hand, blurs the lines between them, and on the other, creates new opportunities for digital modeling. The fundamental characteristic of the nanoworld is its spatiotemporal structure as a set of stable connections that ensure integrity, adaptation and preservation of the basic properties of a self-organized dynamic system under internal and external influences. The goal of the work is to develop an integrative methodology for modeling 3D structures, within the framework of which to create a meta-theory of digital modeling of physical reality.Human activity in real and/or virtual space creates cognitive problems that are caused by a hybrid subject environment. However, the commonality of biological, organic and inorganic substances in the nanoworld makes their structures similar, which made it possible to develop an integrative methodology for modeling 3D structures of information sources of various natures. The methodology is based on the complementarity of paradigms and tools for digital modeling of physical reality, which are united by physical and social ontology.The harmony of perception of triads of conjugate opposites in information sources of various natures is the basis of a hybrid approach to the integrative metatheory of digital modeling of physical reality. Therefore, in extreme conditions, the connection of hybrid thinking with the holographic nature of memory, which also has a semiotic nature, is important. The prospects and innovative potential of the methodology for modeling 3D structures and the complementarity of metamodels of physical reality are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents some metamodeling techniques to analyze the variability of the performances of an inductive power transfer (IPT) system, considering the sources of uncertainty (misalignment between the coils, the variation in air gap, and the rotation on the receiver). For IPT systems, one of the key issues is transmission efficiency, which is greatly influenced by many sources of uncertainty. So, it is meaningful to find a metamodeling technique to quickly evaluate the system’s performances. According to the comparison of Support Vector Regression, Multigene Genetic Programming Algorithm, and sparse Polynomial Chaos Expansions (PCE), sparse PCE is recommended for the analysis due to the tradeoff between the computational time and the accuracy of the metamodel.

Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity. [ABSTRACT FROM AUTHOR]

Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity.

AbstractAs part of the verification, validation, and uncertainty quantification process applied to neutronics deterministic codes, there is a requirement to expand the validation domain, especially to accommodate new third-generation reactors. The objective of the present work is to estimate the numerical biases arising from the several approximations used in deterministic codes and across different points in the phase space. Typically, this is accomplished by comparing the deterministic code to be validated with a Monte Carlo or stochastic reference code (without significant approximations). Since these reference calculations are computationally expensive, this paper proposes an alternative approach for predicting model biases of the APOLLO3® deterministic code for third-generation pressurized water reactors using machine learning algorithms.Three types of metamodels are employed (polynomial regression, kriging, and neural networks). Two scales are investigated, from a single assembly to a cluster of 3 × 3 assemblies [small two-dimensional (2-D) core], with model biases evaluated for APOLLO3 schemes with various levels of accuracy (lattice and core solvers, with high- to low-fidelity approaches). For the small 2-D core, numerical biases are observed for reactivity and power peak, representing both global and local quantities of interest. Throughout the study, the best results are achieved using kriging or neural networks, even if polynomial regression provides satisfactory predictions in some cases. The possibility of predicting biases for different quantities is also introduced.In conclusion, this paper discusses the prospects of extending the applicability of these metamodels to small and large third-generation pressurized water reactor cores, the idea being to potentially use these metamodels to support safety demonstrations for the new reactors in the long term. [ABSTRACT FROM AUTHOR]

In conventional vehicle design approaches, there is typically little understanding of the consequences of early stage design choices. This may be attributed to the conventional approach’s limitations in capturing complex interactions, which leads to increased design iterations. To overcome this, holistic multidisciplinary models have been developed. However, these models introduce the burden of complexity and costs due to their intricate nature. Additionally, it is challenging to gain meaningful insights without a deeper understanding of the model’s structure and behavior. In this article, an alternative form of model representation is proposed to address these shortcomings. This was achieved by integrating two concepts: network theory and sensitivity analysis. A detailed and robust framework is provided, which represents complex multidisciplinary models as network models, reducing their complexity and allowing insights to be extracted from them. This approach was demonstrated through a case study of a rail vehicle traction system, including a traction motor and an inverter, coupled with an operational drive cycle. Among the 246 factors identified in the traction system network model, the three most influential inputs were determined for the selected output factor of interest. Subsequently, the knock-on effects of these inputs were assessed. The results indicate a significant reduction in the network graph size compared to the complete network graph of the traction system model, highlighting a substantial decrease in the number of factors considered in the analysis. This demonstrates the capability of the proposed framework to simplify the analysis while retaining the ability to examine intricate interaction effects.

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control strategies (MPC), the development of dedicated control platforms and models is required. Predictive models for district systems management should provide supply and demand side integrated modelling, high accuracy, generalization capacity and reduced computational times. However, traditionally available MPC solutions do not meet these requirements as simplified models offer short computational times but lack the required accuracy; detailed physics-based models provide satisfactory generalization but at the expense of high computational costs; and the generalization capacity of data models is constrained by the quality and availability of data. In contrast, metamodels developed through the combined use of physics-based models and machine learning techniques offer a powerful alternative at reduced computational cost. This paper describes an upgraded Integrated District Model concept developed through co-simulation coupling metamodels of buildings with a district heating infrastructure Modelica model. Furthermore, the process to produce the metamodels and optimization engine required to generate demand flexibility optimization functionalities for the buildings of the Stepa Stepanovic subnetwork (Belgrade) is depicted. Starting from the development of metamodels of instances of specific buildings (residential and educational use) the process was expanded to provide additional generalization to define, (1) a generic metamodel with the capacity to reproduce the behaviour of any instance of building of the residential typology, and (2) metamodels with generalization capacity in relation to operational settings. As part of this process the potential of several machine learning algorithms (e.g Support Vector Machines, etc) was evaluated including the latest ensemble boosting methods (e.g. Adaboost, Gradient Boosting and Extreme Gradient Boosting) with comparatively low use in the building simulation community. Finally, a virtual test bed consisting in metamodels coupled to an optimization engine based on genetic algorithms, was implemented, and compared to a traditional Physics-based model MPC solution (EnergyPlus-GENOPT), to evaluate the potential of the developed building level optimization functionalities. The metamodels and optimization engine were able to reproduce the optimized settings identified by the EnergyPlus-GENOPT MPC solution with cost savings potentials of 5–10%.

Context. In this research, we explore an ensemble of metamodels that utilizes multivariate signals to generate forecasts. The ensemble includes various traditional forecasting models such as multivariate regression, exponential smoothing, ARIMAX, as well as nonlinear structures based on artificial neural networks, ranging from simple feedforward networks to deep architectures like LSTM and transformers. Objective. A goal of this research is to develop an effective method for combining forecasts from multiple models forming metamodels to create a unified forecast that surpasses the accuracy of individual models. We are aimed to investigate the effectiveness of the proposed ensemble in the context of forecasting tasks with nonstationary signals. Method. The proposed ensemble of metamodels employs the method of Lagrange multipliers to estimate the parameters of the metamodel. The Kuhn-Tucker system of equations is solved to obtain unbiased estimates using the least squares method. Additionally, we introduce a recurrent form of the least squares algorithm for adaptive processing of nonstationary signals. Results. The evaluation of the proposed ensemble method is conducted on a dataset of time series. Metamodels formed by combining various individual models demonstrate improved forecast accuracy compared to individual models. The approach shows effectiveness in capturing nonstationary patterns and enhancing overall forecasting accuracy. Conclusions. The ensemble of metamodels, which utilizes multivariate signals for forecast generation, offers a promising approach to achieve better forecasting accuracy. By combining diverse models, the ensemble exhibits robustness to nonstationarity and improves the reliability of forecasts [ABSTRACT FROM AUTHOR]

The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation from the conceptual model (class diagram). Finally, the XML file and SQL code are generated for creating the OLAP cubes and relational data warehouse, respectively. Using transformation languages like query-view-transformation (QVT) and Acceleo, all model transformations are performed. [ABSTRACT FROM AUTHOR]

The vehicle structure is a highly complex system as it is subject to different requirements of many engineering disciplines. Multidisciplinary optimization (MDO) is a simulation-based approach for capturing this complexity and achieving the best possible compromise by integrating all relevant CAE-based disciplines. However, to enable operative application of MDO even under consideration of crash, various adjustments to reduce the high numerical resource requirements and to integrate all disciplines in a target way must be carried out. They can be grouped as follows: The use of efficient optimization strategies, the identification of relevant load cases and sensitive variables as well as the reduction of CAE calculation time of costly crash load cases by so-called finite element (FE) submodels. By assembling these components in a clever way, a novel, adaptively controllable MDO process based on metamodels is developed. There are essentially three special features presented within the scope of this paper: First, a module named global sensitivity matrix which helps with targeted planning and implementation of a MDO by structuring the multitude of variables and disciplines. Second, a local, heuristic and thus on all metamodel types computable prediction uncertainty measure that is further used in the definition of the optimization problem. And third, a module called adaptive complexity control which progressively reduces the complexity and dimensionality of the optimization problem. The reduction of resource requirements and the increase in the quality of results are significant, compared to the standard MDO procedure. This statement is confirmed by providing results for a FE full vehicle example in six load cases (five crash load cases and one frequency analysis). [ABSTRACT FROM AUTHOR]

Significant research has been conducted in identifying optimal recovery and adaptation decisions in disruptive scenarios using engineering models. In this context, an aspect that has been target of limited research is that of response times. Modelling is expected to grow progressively more complex as it becomes more accurate. Such complexity increases modelling efforts, and the promise of optimal adaptation and recovery may become hindered. The present work discusses the role of modelling fidelities in adaptation and recovery of systems, and in particular that of using a lower fidelity model that enables zero-time analyses of a system. A framework is proposed for using different fidelities in adaptation and recovery, considering system’s decision time requirements. The relevance of this analysis is researched in two traffic networks and results show that multi-fidelity models should be expected to play a key role in increasing the efficiency of optimal adaptation and recovery decisions.

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. This paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed. [ABSTRACT FROM AUTHOR]

Background: Metamodels can address some of the limitations of complex simulation models by formulating a mathematical relationship between input parameters and simulation model outcomes. Our objective was to develop and compare the performance of a machine learning (ML)–based metamodel against a conventional metamodeling approach in replicating the findings of a complex simulation model. Methods: We constructed 3 ML-based metamodels using random forest, support vector regression, and artificial neural networks and a linear regression-based metamodel from a previously validated microsimulation model of the natural history hepatitis C virus (HCV) consisting of 40 input parameters. Outcomes of interest included societal costs and quality-adjusted life-years (QALYs), the incremental cost-effectiveness (ICER) of HCV treatment versus no treatment, cost-effectiveness analysis curve (CEAC), and expected value of perfect information (EVPI). We evaluated metamodel performance using root mean squared error (RMSE) and Pearson's R 2 on the normalized data. Results: The R 2 values for the linear regression metamodel for QALYs without treatment, QALYs with treatment, societal cost without treatment, societal cost with treatment, and ICER were 0.92, 0.98, 0.85, 0.92, and 0.60, respectively. The corresponding R 2 values for our ML-based metamodels were 0.96, 0.97, 0.90, 0.95, and 0.49 for support vector regression; 0.99, 0.83, 0.99, 0.99, and 0.82 for artificial neural network; and 0.99, 0.99, 0.99, 0.99, and 0.98 for random forest. Similar trends were observed for RMSE. The CEAC and EVPI curves produced by the random forest metamodel matched the results of the simulation output more closely than the linear regression metamodel. Conclusions: ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best. Highlights: Decision-analytic models are frequently used by policy makers and other stakeholders to assess the impact of new medical technologies and interventions. However, complex models can impose limitations on conducting probabilistic sensitivity analysis and value-of-information analysis, and may not be suitable for developing online decision-support tools. Metamodels, which accurately formulate a mathematical relationship between input parameters and model outcomes, can replicate complex simulation models and address the above limitation. The machine learning–based random forest model can outperform linear regression in replicating the findings of a complex simulation model. Such a metamodel can be used for conducting cost-effectiveness and value-of-information analyses or developing online decision support tools. [ABSTRACT FROM AUTHOR]

The Generalized Efficient Global Optimization (GEGO) algorithm assisted by Kriging model can solve black-box problem of complex computing. However, a single sampling point obtained in each iteration process may cause longer objective-evaluation time and slower convergence speed in contrast with multi-point sampling optimization methods. For this, a Kriging-based multi-point sequential sampling optimization (KMSSO) method is presented. The proposed method uses uncertainty estimate information of Kriging to construct the multiple-point generalized Expected Improvement (EI) criterion. In optimization cycle, this criterion is maximized to produce the Pareto front data, which will be further screened to obtain final expensive evaluation points. For numerical tests and an engineering case, KMSSO is compared to GEGO and HAM algorithm and is shown to deliver better results. It is also proves that when multiple points are added per cycle, the optimization accuracy and convergence property are both improved. [ABSTRACT FROM AUTHOR]

Optimal scheduling of pumps in water distribution systems (WDSs) entails reducing operational cost while supplying the required water quality and quantity. The combined use of pumps, however, can increase breakage rate of aging pipes due to high internal pressure. Multi-objective optimization (MO) is crucial in the determination of a trade-off between the two objective functions, minimization of the operational cost and maximization of the velocity reliability index. The velocity reliability index is used as a surrogate metric to quantify the structural performance of the pipes. The optimization process requires repetitive hydraulic simulations resulting in high computational cost. This paper proposes a Gaussian-Process (GP) based sequential approaches that efficiently estimate the optimal Pareto front with reduced computational effort. The technique simultaneously optimizes the two objective functions over a box-constrained domain where each GP model is fitted independently through an infill criterion that balances the space exploration (search of new observations) and exploitation (local improvement around existing observations). The reduced computational cost allows running full hydraulic simulations during the optimization process permitting real time decision making for pumps schedule in large complex WDSs. Utility of the proposed technique was applied for Asmara's WDSs, composed of 9 pumping stations and 12 storage tanks, and showed good performance of the GP based optimization compared to traditional evolutionary optimization techniques (such as NSGA2 and Particle Swarm Optimization). The GP-MO only requires 20 iterations to identify the optimal Pareto front while, even with more than 1000 generations, the NSGA2 is not getting to find a good agreement between the two objective functions. [ABSTRACT FROM AUTHOR]

The successful implementation of Structural Health Monitoring (SHM) systems is confined to the capability of evaluating their performance, reliability, and durability. Although there are many SHM techniques capable of detecting, locating and quantifying damage in several types of structures, their certification process is still limited. Despite the effort of academia and industry in defining methodologies for the performance assessment of such systems in recent years, many challenges remain to be solved. Methodologies used in Non-Destructive Evaluation (NDE) have been taken as a starting point to develop the required metrics for SHM, such as Probability of Detection (POD) curves. However, the transposition of such methodologies to SHM is anything but straightforward because additional factors should be considered. The time dependency of the data, the larger amount of variability sources and the complexity of the structures to be monitored exacerbate/aggravate the existing challenges, suggesting that much work has still to be done in SHM. The article focuses on the current challenges and barriers preventing the development of proper reliability metrics for SHM, analyzing the main differences with respect to POD methodologies for NDE. It was found that the development of POD curves for SHM systems requires a higher level of statistical expertise and their use in the literature is still limited to few studies. Finally, the discussion extends beyond POD curves towards new metrics such as Probability of Localization (POL) and Probability of Sizing (POS) curves, reflecting the diagnosis paradigm of SHM. [ABSTRACT FROM AUTHOR]

Significant research has been conducted in identifying optimal recovery and adaptation decisions in disruptive scenarios using engineering models. In this context, an aspect that has been target of limited research is that of response times. Modelling is expected to grow progressively more complex as it becomes more accurate. Such complexity increases modelling efforts, and the promise of optimal adaptation and recovery may become hindered. The present work discusses the role of modelling fidelities in adaptation and recovery of systems, and in particular that of using a lower fidelity model that enables zero-time analyses of a system. A framework is proposed for using different fidelities in adaptation and recovery, considering system's decision time requirements. The relevance of this analysis is researched in two traffic networks and results show that multi-fidelity models should be expected to play a key role in increasing the efficiency of optimal adaptation and recovery decisions. [ABSTRACT FROM AUTHOR]

Isothermal hot compression tests were conducted on Al 2014 + 2 wt% TiB2 composite at different temperatures (300–450°C) and strain rates (0.001–1 s−1). Using the compression test data, an attempt was made to establish the constitutive relationship for the material by employing three different metamodels such as response surface methodology (RSM), genetic algorithm (GA), and artificial neural network (ANN). The capability of these metamodels towards establishing the non-linear constitutive relation between the process parameters such as temperature, strain rate and strain and flow stress has been ascertained using standard statistical parameters, namely, correlation coefficient (R) and average absolute relative error (AARE). A cubic regression equation was suggested by RSM, while 3–15-1 neural network architecture provided a better correlation. The results obtained are presented and discussed here. [ABSTRACT FROM AUTHOR]

Like any software artifacts, metamodels are evolving entities that constantly change over time for different reasons. Changing metamodels by keeping them consistent with other existing artifacts is an error-prone and tedious activity without the availability of automated support. In this paper, we foster the adoption of metamodel refactorings collected in a curated catalog. The Edelta framework is proposed as an operative environment to provide modelers with constructs for specifying basic refactorings and evolution operators, to define a complete metamodel refactoring catalog. The proposed environment has been used to implement the metamodel refactorings available in the literature and make them executable. A detailed discussion on how modelers can use and contribute to the definition of the catalog is also given. [ABSTRACT FROM AUTHOR]

Daylight harvesting is a well-known strategy to address building energy efficiency. However, few simplified tools can evaluate its dual impact on lighting and air conditioning energy consumption. Artificial neural networks (ANNs) have been used as metamodels to predict energy consumption with high precision, few input parameters and instant response. However, this approach still lacks the potential to estimate consumption when there is daylight harvesting, at the ambient level, where the effect of orientation can be noted. This study investigates this potential, in order to evaluate the applicability of ANNs as a tool to aid the architectonic design. The ANNs were approached as metamodels trained based on EnergyPlus thermo-energetic simulations. The network configuration focused on determining its simplest feasible form. The input parameters adopted as the main variables of the building envelope were as follows: orientation, window-to-wall ratio and visible transmission. The effects of the encoding of orientation as a network input parameter, the number of examples of each variable for network training and changing the parameters used for the training were evaluated. The networks predicted the individualized consumption according to the end use with errors below 5%, indicating their potential to be applied as a simplified tool to support the design process, considering the elementary variables of the building envelope. The discussion of results focused on guidelines and challenges to achieve this purpose when contemplating the broadening of the metamodel scope.

The optimum structural design of real-world 3D concrete building frames to modern design standards is a complex and computationally expensive task. Hence, the use of surrogate-based optimization (SBO) methodologies must be investigated to reduce computational cost. The present study applies, for first time, a fully-fledged SBO algorithm to the optimum design of 3D concrete building frames. More particularly, the algorithm is applied to the minimum material cost design of a 4-storey and a 12-storey 3D RC building according to Eurocodes. It is found that the SBO algorithm can converge earlier than other well-established metaheuristic optimization algorithms reducing considerably the required computational effort. Nevertheless, it is likely to get trapped in local optima for large-scale RC frames. To overcome this drawback, a novel hybrid approach is also proposed herein that offers improved computational performance for large-scale concrete building frames.

Theories of knowledge reuse posit two distinct processs: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization . Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community’s design knowledge available for reuse for customization—or further extension of the metamodels, a kind of reuse for innovation. [ABSTRACT FROM AUTHOR]

An effective method for solving a class of dynamic-reliability-based design optimization (DRBDO) problems is proposed in the present paper. Failure probability functions and their sensitivities with respect to the design variables are estimated in the framework of the probability density evolution method (PDEM). In particular, a PDEM-based metamodel-refined approach is defined in an augmented input space to improve the efficiency of failure probability estimations and sensitivity analyses. Moving trust regions are imposed on the augmented input space to ensure the accuracy of the metamodel. To solve the optimization problems, the PDEM-based metamodel-refined approach is embedded into a feasible direction interior point scheme. In this scheme, a feasible search direction is first obtained by solving the perturbed Karush–Kuhn–Tucker (KKT) conditions. Then, a line search technique, which is consistent with the PDEM-based metamodel-refined approach, is employed to speed up the convergence of the optimization process. The results of the numerical examples indicate that the proposed method is a competitive choice for solving a class of DRBDO problems with a small number of reliability and structural analyses. [ABSTRACT FROM AUTHOR]

Usage of rolling contact bearings in variety of rotor-dynamic applications has put forth a need to develop a detailed and easy to implement techniques for the assessment of damage related features in these bearings so that before mechanical failure, maintenance actions can be planned well in advance. In accordance to this, a method based on dimensional amplitude response analysis and scaling laws is presented in this paper for the diagnosis of defects in different components of rolling contact bearings in a dimensionally scaled rotor-bearing system. Rotor, bearing, operating and defect parameters involved are detailed for dimensional analysis using frequency domain vibration data. A defect parameter for modeling all the three dimensions of the defect as well as the different shapes like square, circular, rectangular is put forth which takes into account the volume as well as the surface area of the defect. Experimental data set is generated for the 'model' bearing (designated as SKF30205J2/Q) using Box-Behnken design of response surface methodology for solution of the theoretical model by factorial regression approach. Obtained metamodel is then used for the prediction of the objective variable, i.e., Vibration acceleration amplitude at the defect frequency component for other types of 'test' bearings (designated as SKF 30305C and SKF 22220 EK) using the developed scaling laws. Confirmation experiments showed that the computable relationship amongst objective variable and the dimensionless parameters can be forecast and correlated. [ABSTRACT FROM AUTHOR]

The most significant goals of this paper are: (i) to provide additional insight on the influence of hull form parameters on seakeeping performance of typical Mediterranean fishing vessels; (ii) to develop efficient and reliable metamodels for fast and reliable evaluation of seakeeping performance in a multiattribute decision-making environment; (iii) to facilitate and speed up the selection of the design with the best possible seakeeping performance at conceptual design stage. To enhance the accuracy and applicability of predictions of seakeeping characteristics and to find out guidelines for design scope, new metamodels have been developed from an extended database of 57 cases, which comprehends the hull form geometrical data, heave (h), pitch (p) and vertical acceleration (av) rms values of both old 39 cases studied previously as well as new 18 cases of Mediterranean coastal fishing vessels previously analysed for resistance assessment. Main statistical parameters of the derived regression equations show a substantial improvement of statistical accuracy in h, p and av estimates, particularly when geometrical descriptors of forebody and afterbody hull form are included as independent variables. Statistical homogeneity of the N57 extended database was verified by cluster analysis of selected responses with classical hull form coefficients/parameters, and new descriptors data altogether taken as discriminant variables in two nucleus clustering analysis. Values of heave, pitch and vertical acceleration yielded by the metamodels have been compared to the ones determined by means of direct computations. Low residuals have confirmed reliability of the proposed prediction metamodels to determine since conceptual design stage seakeeping performance of fishing vessels with hull forms and main dimensions which could be included in the design space defined by the population forming the extended database. [ABSTRACT FROM AUTHOR]

Water transit time is the duration between the entry and exit of a parcel of water across a hydrologic system. It is a fundamental characteristic that links hydrologic transport, biogeochemical processing, and water quality, and it has broad implications for resource vulnerability and sustainability. Physically based models can accurately describe transit time distributions but require significant computational resources when applied to large regions at high resolutions. In this study, we evaluate the potential of machine learning metamodels to emulate physically based models for computationally efficient prediction of key metrics from transit time distributions. Transit times are computed from a continental scale, integrated hydrologic model coupled with particle tracking. The metamodeling approach is illustrated in the 280,000-sq km Upper Colorado River Basin, USA, a principal headwater basin that is under multiple stresses, including resource overallocation, water quality threats, and climate change impacts. We evaluate the effects of using different types of spatial aggregation in the metamodels, including regular grids, hydrologic units, and upstream watersheds. We found that metamodels using upstream watershed aggregation exhibited the best overall performance across our target predictions. Errors were more pronounced in metamodels that employed smaller spatial aggregation units compared to larger units, suggesting that additional predictors that capture the heterogeneity of topographic, climatic, and geologic properties are needed at these scales. We also found that predictor importance and input-output relations were remarkably consistent across spatial aggregation type and agree with previous findings documented from physically based models and tracer-based studies. Our results show the feasibility of developing machine learning metamodels for predicting transit times and demonstrate the necessity of multiscale analyses to probe the robustness of the findings.

Computer experiments are often used as inexpensive alternatives to real‐world experiments. Statistical metamodels of the computer model's input‐output behavior can be constructed to serve as approximations of the response surface of the real‐world system. The suitability of a metamodel depends in part on its intended use. While decision makers may want to understand the entire response surface, they may be particularly keen on finding interesting regions of the design space, such as where the gradient is steep. We present an adaptive, value‐enhanced batch sequential algorithm that samples more heavily in such areas while still providing an understanding of the entire surface. The design points within each batch can be run in parallel to leverage modern multi‐core computing assets. We illustrate our approach for deterministic computer models, but it has potential for stochastic simulation models as well. [ABSTRACT FROM AUTHOR]

With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable cloud system. Several standards have been proposed to address this challenge and provide a unified interface to cloud resources. The Open Cloud Computing Interface (OCCI) thereby focuses on the standardization of a common API for Infrastructure-as-a-Service (IaaS) providers, while the Topology and Orchestration Specification for Cloud Applications (TOSCA) focuses on the standardization of a template language to enable the proper definition of the topology of cloud applications and their orchestrations on top of a cloud system. TOSCA thereby does not define how the application topologies are created on the cloud. Therefore, we analyze the conceptual similarities between the two approaches and we study how we can integrate them to obtain a complete standard-based approach to manage both Cloud Infrastructure and Cloud application layers. We propose an automated extensive mapping between the concepts of the two standards, and we provide TOSCA Studio, a model-driven tool chain for TOSCA that conforms to OCCI. TOSCA Studio allows to graphically design cloud applications as well as to deploy and manage them at runtime using a fully model-driven cloud orchestrator based on the two standards. Our contribution is validated by successfully transforming and deploying three cloud applications: WordPress, Node Cellar and Multi-Tier. [ABSTRACT FROM AUTHOR]

Historical masonry arch bridges constitute the backbone of many existing transportation networks in different countries in Europe and worldwide. They represent valuable cultural heritage assets and play an essential social and economic role. Since construction, old masonry bridges have accumulated structural damage from traffic and environmental actions. Furthermore, depending on their geometrical and mechanical characteristics, they may be particularly vulnerable to extreme events like earthquakes. Thus, accurate structural assessment under different loading conditions is critical for the conservation of these structures. Realistic assessment requires suitable numerical models to represent the characteristic 3D behaviour. The complexity of this task is further compounded by the practical difficulty in obtaining essential information on the internal bridge structure and the masonry mechanical parameters, which are vital to achieve accurate response predictions against service and extreme actions. This paper presents an advanced calibration procedure for a refined macroscale bridge model, allowing for the anisotropic nature of the masonry material. The proposed calibration approach is applied to an actual multi-span masonry viaduct, where sonic, ultrasonic, and ground penetrating radar tests are conducted to investigate the internal structure of the viaduct and determine the elastic properties of the masonry materials. In addition, the dynamic characteristics of the bridge are evaluated through in-situ measurements under environmental vibrations and used for model validation. The results from a standard simplified model calibration and an enhanced calibration are compared considering the vibration modes of the bridge. Simplified calibration is carried out using the results from in-situ tests, while a statistic inference procedure and numerical optimisation are adopted in the refined calibration to achieve improved accuracy. Although the paper focuses on a specific case study, the adopted methodology can be easily applied to studying other masonry bridges and cultural heritage masonry structures. • An advanced calibration procedure for a refined bridge macroscale model is presented. • The proposed calibration approach is applied to a multi-span masonry viaduct. • In-situ non-destructive tests are used to evaluate the internal structure of the bridge and the elastic material parameters. [ABSTRACT FROM AUTHOR]

Abstract Common goals of modern production processes are precision and efficiency. Typically, they are conflicting and cannot be optimized at the same time. Multi-objective optimization methods are able to compute a set of good parameters, from which a decision maker can make a choice for practical situations. For complex processes, the use of physical experiments and/or extensive process simulations can be too costly or even unfeasible, so the use of surrogate models based on few simulations is a good alternative. In this work, we present an integrated framework to find optimal process parameters for a laser-based material accumulation process (thermal upsetting) using a combination of meta-heuristic optimization models and finite element simulations. In order to effectively simulate the coupled system of heat equation with solid-liquid phase transitions and melt flow with capillary free surface in three space dimensions for a wide range of process parameters, we introduce a new coupled numerical 3d finite element method. We use a multi-objective optimization method based on surrogate models. Thus, with only few direct simulations necessary, we are able to select Pareto sets of process parameters which can be used to optimize three or six different performance measures.

Modern models of multicultural relations in the world in general and in Eurasia in particular are becoming more complex and confusing. The need to order a huge number of concepts, theories and approaches is increasingly evident. Modern researchers working in the field of intercultural relations («communications») are actively building integrative models and metamodels to optimize the relationships between communities and people of different ethnic, national, religious, etc. groups. The need for such models and meta-models is due to the fact that the modern age is the age of multicultural communities, the most active migrations and the revival of the nomadic way of life in general.

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are many barriers to using Bayesian calibration in health decision sciences stemming from the need to program complex models in probabilistic programming languages and the associated computational burden of applying Bayesian calibration. In this paper, we propose to use artificial neural networks (ANN) as one practical solution to these challenges. Methods: Bayesian Calibration using Artificial Neural Networks (BayCANN) involves (1) training an ANN metamodel on a sample of model inputs and outputs, and (2) then calibrating the trained ANN metamodel instead of the full model in a probabilistic programming language to obtain the posterior joint distribution of the calibrated parameters. We illustrate BayCANN using a colorectal cancer natural history model. We conduct a confirmatory simulation analysis by first obtaining parameter estimates from the literature and then using them to generate adenoma prevalence and cancer incidence targets. We compare the performance of BayCANN in recovering these "true" parameter values against performing a Bayesian calibration directly on the simulation model using an incremental mixture importance sampling (IMIS) algorithm. Results: We were able to apply BayCANN using only a dataset of the model inputs and outputs and minor modification of BayCANN's code. In this example, BayCANN was slightly more accurate in recovering the true posterior parameter estimates compared to IMIS. Obtaining the dataset of samples, and running BayCANN took 15 min compared to the IMIS which took 80 min. In applications involving computationally more expensive simulations (e.g., microsimulations), BayCANN may offer higher relative speed gains. Conclusions: BayCANN only uses a dataset of model inputs and outputs to obtain the calibrated joint parameter distributions. Thus, it can be adapted to models of various levels of complexity with minor or no change to its structure. In addition, BayCANN's efficiency can be especially useful in computationally expensive models. To facilitate BayCANN's wider adoption, we provide BayCANN's open-source implementation in R and Stan. [ABSTRACT FROM AUTHOR]