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The urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

In multi-lamp, high-flux solar simulators, the flux profile can be affected by the choice of lamp, reflector, and a variety of geometric design parameters, some of which can be altered after construction. In this work, a novel procedural optimisation method is applied to reconfigure the positions of lamps and reflectors in a high-flux solar simulator to match a desired target flux profile. A 10-module high-flux solar simulator using 2.5 kWe metal halide lamps and ellipsoidal reflectors is modeled. Lamp modules are simulated with Monte Carlo ray tracing and the resulting flux profiles are parameterised by curve fitting to create a surrogate model which can rapidly generate an accurate flux. The surrogate model is integrated into a trust-region reflective systematic optimisation which determines a set of lamp module parameters for each of the ten lamps, providing a close approximation of a user-defined flux profile. [ABSTRACT FROM AUTHOR]

This work presents the multi-objective optimization (MOO) of the dynamic performance of a suspended monorail vehicle (SMV) moving on a curved bridge, based on an effective surrogate model. First, the vehicle-bridge dynamic interaction features are analysed through a dynamic model. Then, an MOO method is developed based on an effective surrogate model. In this method, the accuracy and stability of the radial basis function are enhanced by proposing an innovative improvement strategy and adopting the particle swarm optimization algorithm. The proposed method is validated by a few numerical tests. Based on this verification, the MOO model between key parameters and several optimization objectives is formulated, and the optimization solution set of the vehicle key parameters is obtained using the non-dominated sorting genetic algorithm II. Finally, the optimization effects are revealed by comparing the dynamic performance of the vehicle obtained from the original values and the optimal solution set. [ABSTRACT FROM AUTHOR]

This study presents a multi-output Gaussian process regression (GPR) surrogate model for seismic-fragility analysis of bridge structural systems. Multi-output GPR can model the correlations among multiple outputs, accuracy and stability are achieved with fewer training data, which reduces the computational cost of fragility analysis. Furthermore, the explainability of the constructed surrogate model is implemented by adopting an automatic relevance-determination (ARD) kernel in the GPR. The estimated hyperparameters can provide the contribution of the uncertainty of each input parameter to the outputs. The fragility analysis using the multi-output GPR surrogate model was verified by applying it to a seismic isolation highway bridge with multiple spans and a curved geometry. The effectiveness of the multi-output GPR was demonstrated by the construction of an accurate and stable surrogate model with 46 inputs and 28 outputs. The relative contributions of the uncertainties to the structural properties and input earthquake loads could also be understood. The fragility curves, at both the component and system levels, were appropriately obtained using a sufficient number of samples in a Monte Carlo calculation. Furthermore, the failure modes were evaluated, identifying which structural components contributed to the system failure. This enabled discussions on structural system failures from the viewpoint of the structural dynamic characteristics of the bridge and earthquake-load properties. [ABSTRACT FROM AUTHOR]

An XY-compliant mechanism is a two-degree of freedom positioner which is considered to have some distinguishing characteristics in ultra-precision technology. To ensure a long working life, it is necessary to study the fatigue life, however, in the related works of the XY-compliant mechanism, no research into design optimization for maximum fatigue life has been conducted so far. Moreover, the sample size of fatigue data is often inadequate to develop a surrogate model. This paper pioneers a new method for maximizing the fatigue life of the proposed mechanism, in the context of small-size fatigue life samples. Particularly, the Synthetic Minority Oversampling Technique is utilized to enlarge the sample size. A machine learning technique is then applied to create the surrogate model, in which fatigue life is considered the output. The Hunger Games Search is then used to maximize the output. In this study, Steel A36 and AL 6061T6 materials are utilized for the mechanism. The numerical results of two case studies show that using Synthetic Minority Oversampling Technique can predict appropriately the fatigue life, compared to using the original data. Specifically, after taking the natural logarithm of fatigue life, the average mean square error when using simulated data is 1.8 % and 36.8 % better than when using original data, for Case 1 and Case 2, respectively. The optimal fatigue life found by the proposed method compared with the baseline is about 350 % for Case 1 and 1250 % for Case 2, respectively. The optimal findings are also confirmed using ANSYS software, in which the errors of fatigue life are less than 5 % . [ABSTRACT FROM AUTHOR]

Buffer allocation, which is an important research topic in manufacturing system design, typically focuses on system performance and cost. However, few previous studies have been performed to investigate energy-saving buffer allocation, which can decrease operational energy consumption in green manufacturing. Furthermore, the computational efficiency of solving the buffer allocation problem requires further investigation. This paper proposes a data-driven hybrid algorithm based on multi-evolutionary sampling strategies for solving energy-saving buffer allocation that can maximize the throughput rate and minimize energy consumption. Two evolutionary sampling strategies, that is, global search and surrogate-assisted local search, are integrated to balance exploitation and exploration. In addition, a database containing historical data pertaining to buffer allocation solutions is used to develop surrogate models that can rapidly predict the throughput and energy consumption and improve the evaluation efficiency of the local search strategy. Experimental results demonstrate the efficacy of the proposed algorithm. This study contributes to an efficient buffer allocation and presents a new perspective on energy-saving measures for green manufacturing designs. [ABSTRACT FROM AUTHOR]

This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a multi-dimensional function along a one-dimensional line segment of interest. The method does not rely on derivative information and the function to be learned can be a computationally-expensive "black box" function that must be queried via simulation or other means. It is assumed that the underlying function is noise-free and smooth, although the algorithm can still be effective when the underlying function is piecewise smooth. The method constructs a smooth surrogate on a set of equally-spaced grid points by evaluating the true function at a sparse set of judiciously chosen grid points. At each iteration, the surrogate's non-tabu local minima and maxima are identified as candidates for sampling. Tabu search constructs are also used to promote diversification. If no non-tabu extrema are identified, a simple exploration step is taken by sampling the midpoint of the largest unexplored interval. The algorithm continues until a user-defined function evaluation limit is reached. Numerous examples are shown to illustrate the algorithm's efficacy and superiority relative to state-of-the-art methods, including Bayesian optimization and NOMAD, on primarily nonconvex test functions. [ABSTRACT FROM AUTHOR]

Congestion pricing is one of the efficient travel demand management strategies. Many existing researches focus on dealing with the toll optimization problem for a single area. However, the urban network is often composed of several administrative regions. Furthermore, even inside a single administrative region, there may be multiple subnetworks with different traffic dynamics. As a result, the centric pricing scheme may not be applicable. This paper aims to design a coordinated dynamic pricing scheme for such a scenario with multiple adjacent areas which experience an overlapping congested period. Unlike the traditional approach centered on the bi-level mathematical programming, we adopt the regressing Kriging model to estimate the input–output mapping, thus searching for the simulation-based optimal solution in the toll design problem. Two types of coordinated pricing schemes are proposed. The first or unconstrained scheme only focuses on the network performance, while the second or constrained scheme further takes into account the pricing efficiency. The proposed coordinated pricing scheme is further compared with the perimeter control. The results indicate that our scheme is more moderate without imposing traffic burden on the links/corridors heading to protected zones. [ABSTRACT FROM AUTHOR]

AbstractBuckling behavior of laminated composite shells changes drastically with introduction of cut outs. The present work aims to predict the buckling behavior of bio-inspired helicoidal laminated composite cantilever cylindrical shells using finite element-assisted multi-output Support Vector Machine (SVM) learning algorithm. The model is trained to predict the value for the non-dimensional critical buckling load with the number of layers, geometry of shell, orientation and thickness of each layer, and dimension of circular cut out are adopted as input parameters with critical buckling load for perfect, shell containing one circular or square hole at the center of the length, and shell containing two circular or square hole at the center of the length of the shell are predicted. Thus, single surrogate can predict critical buckling load for five cases in a single rum. Two different types of cut outs i.e., circular and square shaped cut outs are studied. Influence of helicoidal scheme, number of layers, and dimension and number of holes on the buckling behavior of shell is studied. For shell without cut outs, conventional layup schemes such as cross-ply and quasi-isotropic are found to give highest values of the non-dimensional critical buckling load compared to the helicoidal schemes. For shells with cut outs, Fibonacci helicoidal and helicoidal semi-circular (HS3) schemes gives maximum values for the non-dimensional critical buckling loads. [ABSTRACT FROM AUTHOR]

In this paper, deep learning–based data‐driven surrogate modeling approach is proposed for enhancing cost‐efficiency of multiband antenna design optimization. The proposed surrogate model–assisted design approach has achieved a computational cost reduction of almost 40% compared to the conventional direct electromagnetic solver–based design methodologies in case of single design example. As for the validation of the proposed method, the obtained optimal design parameters from the surrogate model are used to manufacture an antenna design. The obtained results from the experimental measurement are compared with counterpart results from the literature. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies. [ABSTRACT FROM AUTHOR]

Summary: A surrogate FRAX® model for Nepal has been constructed using age- and sex-specific hip fracture rates for Indians living in Singapore and age- and sex-specific mortality rates from Nepal. Introduction: FRAX models are frequently requested for countries with little or no data on the incidence of hip fractures. In such circumstances, the development of a surrogate FRAX model is recommended based on country-specific mortality data but using fracture data from a country, usually within the region, where fracture rates are considered to be representative of the index country. Objective: This report describes the development and characteristics of a surrogate FRAX model for Nepal. Methods: The FRAX model used the ethnic-specific incidence of hip fracture in the Indian community of Singapore, combined with the death risk for Nepal in 2015–2019. The number of hip fractures in 2015 and 2050 was estimated based on the United Nations' predicted changes in population demography. Results: The surrogate model gave similar hip fracture probabilities to estimates from Sri Lanka, India and Pakistan but lower 10-year fracture probabilities for men and women at older ages compared to the model for Singapore, reflecting a higher mortality risk in Nepal compared with Singapore. There were very close correlations in fracture probabilities between the Nepalese and the Singapore models (r> 0.995) so that the use of the Nepalese model had little impact on the rank order of risk, i.e. a person at the xth percentile of risk with one model will be at the xth percentile of risk with the other. It was estimated that 6897 hip fractures arose in 2015 in individuals aged 50 years and older in Nepal, with a predicted 3-fold increase expected by 2050, when 23,409 hip fractures are expected nationally. Conclusion: The surrogate FRAX model for Nepal provides an opportunity to determine fracture probability within the Nepalese population and help guide decisions about treatment. [ABSTRACT FROM AUTHOR]

Seismic hazard analyses in the area of a nuclear installation must account for a large number of uncertainties, including limited geological knowledge. Combining the accuracy of physics-based simulations with the expressivity of deep neural networks can help to quantify the influence of crustal geological uncertainties on surface ground motion. This work uses a Factorised Fourier Neural Operator (F-FNO) to learn the relationship between 3D crustal heterogeneous geologies and time-dependent wavefields at the Earth's surface up to a 5 Hz maximal frequency. The F-FNO is pretrained on a generic database and then, fine-tuned with only 250 samples targeted to the Le Teil region (South-Eastern France). The F-FNO correctly predicts the wave arrival times and the main wavefronts. As quantified by Goodness-Of-Fit (GOF) criteria, 83% of predictions have excellent phase GOF and 75% have very good envelope GOF. The Peak Ground Velocity and Pseudo-Spectral Acceleration are also accurate, especially for geologies with low-to-moderate heterogeneities. Thanks to the F-FNO speed, ground motion distributions can be easily computed and provide safety margins compared to 1D simulations. These results show that the F-FNO is an efficient surrogate model to quantify the range of ground motion a nuclear installation could face in the presence of geological uncertainties. [ABSTRACT FROM AUTHOR]

Disaster resilience is an emerging concept for managing the risk of civil structural systems considering not only structural safety against extreme loads but also aftermath recovery efforts. The system reliability–based resilience analysis framework facilitates the quantitative evaluation of the disaster resilience performance of structural systems by assessing reliability and redundancy for numerous disruption scenarios. However, its practical application is limited due to the substantial number of structural analyses required to estimate the reliability and redundancy indices. To address the computational challenges, this paper proposes a new importance sampling algorithm, termed Importance Sampling for Noteworthy Scenarios (ISNS). The ISNS algorithm leverages a Gaussian process–based principal point search method to identify initial disruption scenarios that dominantly impact the structure's resilience. Subsequently, a mixture-based distribution is constructed to represent the near-optimal importance sampling density characterizing the failure domains of the noteworthy disruption scenarios. The two-step procedure enables the simultaneous estimation of both reliability and redundancy indices of the identified noteworthy scenarios. Furthermore, an active learning scheme is incorporated to efficiently train surrogates. Numerical examples of engineering applications are investigated to demonstrate the improved efficiency offered by the proposed method. However, the proposed method faces limitations in multihazard contexts and high-dimensional, highly nonlinear scenarios. These limitations necessitate further validation of the Gaussian process and mixture models to ensure robustness. [ABSTRACT FROM AUTHOR]

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In order to improve the separation control effect of an oscillating jet, the external flow field of the actuators and the wing wake are obtained via hot-wire measurements to optimize the actuator and achieve wing separation flow control. The optimization objectives are to improve the sweeping uniformity and range of the jet. In the present study, the PCA method is used for the modal decomposition of the velocity distribution. The modal-based actuator evaluation parameters are proposed, and the kriging surrogate models of the modal coefficients (principal components) on the actuator parameters are established. The multi-objective genetic algorithm was utilized to complete the optimization of the actuator, and the effect of flow separation control on the wing was verified. The results show that three patterns exist in the time-averaged velocity distribution of the external flow field: unimodal, broad and bimodal, from unimodal to bimodal, the degree of the jet sweeping uniformity gradually decreases, and the sweeping range gradually increases. The pattern of the velocity distribution modals affects the degree of jet sweeping uniformity, while the distance of the modal peaks affects the jet sweeping range. The two evaluation parameters are negatively correlated: insufficient sweeping range or poor sweeping uniformity of the jet are not conducive to wing separation flow control, and the two must be coordinated to achieve the optimal control effect. [ABSTRACT FROM AUTHOR]

In this paper, we present a physics- and data-driven study on the ground effect on the propulsive performance of tandem flapping wings. With numerical simulations, the impact of the ground effect on the aerodynamic force, energy consumption, and efficiency is analyzed, revealing a unique coupling effect between the ground effect and the wing–wing interference. It is found that, for smaller phase differences between the front and rear wings, the thrust is higher, and the boosting effect due to the ground on the rear wing (maximum of 12.33%) is lower than that on a single wing (maximum of 43.83%) For a larger phase difference, a lower thrust is observed, and it is also found that the boosting effect on the rear wing is above that on a single wing. Further, based on the bidirectional gate recurrent units (BiGRUs) time-series neural network, a surrogate model is further developed to predict the unsteady aerodynamic characteristics of tandem flapping wings under the ground effect. The surrogate model exhibits high predictive precision for aerodynamic forces, energy consumption, and efficiency. On the test set, the relative errors of the time-averaged values range from −4% to 2%, while the root mean squared error of the transient values is less than 0.1. Meanwhile, it should be pointed out that the established surrogate model also demonstrates strong generalization capability. The findings contribute to a comprehensive understanding of the ground effect mechanism and provide valuable insights for the aerodynamic design of tandem flapping-wing air vehicles operating near the ground. [ABSTRACT FROM AUTHOR]

This paper presents an integrated passive device (IPD) structure based on fan-out wafer-level packaging (FOWLP) for the front end of frequency-modulated continuous wave (FMCW) radar systems, focusing on enhancing the integration efficiency and performance of large passive components like antennas. Additionally, a new metric is introduced to assess this structure's effect on the average noise figure in FMCW systems. Using this metric as a loss function, we apply the support vector machine (SVM) for electromagnetic simulation and the genetic algorithm (GA) for optimization. The sample fitting variance is 2.42 dB, reducing computation time from 12 min to under 1 millisecond, with the entire optimization completed in less than 100 s. The optimized IPD structure is 0.7 × 0.9 × 0.014 λ 0 3 in size and achieves over 35 dB isolation between the transmitter and receiver. Compared to the IPD model calculated by empirical formulas, the optimized device lowers the average noise figure by 15.2 dB and increases maximum gain by 4.19 dB. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to use parametric human modeling and machine learning methods to identify representative occupants that can account for injury variations among a more diverse population with a limited simulation budget. Method: A maximal projection method was used to sample 100 occupants, considering the variations in stature, weight, and sitting height. An automated mesh morphing method was used to morph the THUMS v4.1 midsize male model into the target geometries. US-NCAP frontal crash simulations were conducted with morphed human models and validated vehicle/restraint models. Surrogate models based on the Gaussian Process (GP) method were trained to find inducing points (IP), here defined as a small number of representative occupants whose outcomes could be used to accurately estimate the variations in the injury risks and patterns throughout the population. Statistical analysis was conducted to validate the IPs' coverage of total variation by illustrating the IP distribution. Restraint optimization was performed at IPs to yield an enhanced restraint system. The method was validated through comparisons among the predicted injury risks under the optimal and baseline designs. Results: Only 20 IPs were needed to sufficient to properly represent the variations in the injury risks and patterns in the whole population with acceptable accuracy. Compared to the surrogate model built from 100 crash simulations, the IP-based surrogate models incurred only 0.4% and 1.8% errors in head injury risks for males and females, respectively. Regarding the injury risks on the chest and lower extremities, the IP-based surrogate models resulted in less than 0.1% and 0.5% errors for males and females, respectively. The FE simulations indicated that the optimal restraint system design reduced the injury risk by relatively 16% and 13% for male and female respectively when delta-V = 25 (mph), and 47% and 27% for male and female when delta-V = 35 (mph). Significance of results: The study proposed a method to generate more accurate injury risk predictions for a more diverse population under a limited simulation budget. Simulations using IPs may enable restraint system optimization to be conducted more efficiently while reducing injury risks across a more diverse population. [ABSTRACT FROM AUTHOR]

Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed based on the numerical simulation results and strain responses captured from the distributed fiber optic sensors. Finally, a multi-island genetic algorithm is employed for global optimization to identify the size and location of the damage. The efficacy of the proposed method is validated through numerical examples and experiment studies, examining the correlations between damage location, damage size, and strain responses. The findings confirm that the model updating technique, in conjunction with distributed fiber optic sensors, can precisely identify delamination in composite structures. [ABSTRACT FROM AUTHOR]

Numerical simulations are crucial for predicting outcomes in forging processes but often neglect dynamic interactions within forming tools and presses. This study proposes an approach for achieving accurate real-time prediction of forging outcomes. Initially, a simulation-based surrogate model is developed to replicate key process characteristics related to the billet, enabling prediction of geometry, deformation field, and forging load after an upsetting operation. Subsequently, this model is integrated with a mass-spring-damper model representing the behavior of forging machine and tools. This integration enables the prediction of blow efficiency and energy distribution after each blow, including plastic, elastic, damping, and frictional energy of the upsetting operation. The approach is validated by comparing predictions with experimental results. The coupled model outperformed Finite Element Method (FEM) predictions, exhibiting mean absolute errors (MAE) below 0.1 mm and mean absolute percentage errors (MAPE) below 1% in geometry predictions. Deformation field predictions showed errors below 0.05 mm/mm, and load-displacement curves closely matched experimental data. Blow efficiency predictions aligned well with experimental results, demonstrating a mean absolute error below 1.1%. The observed energy distribution correlated with literature findings, underscoring the model’s fidelity. The proposed methodology presents a promising approach for accurate real-time prediction of forging outcomes. [ABSTRACT FROM AUTHOR]

For wire arc additive manufacturing (WAAM) process the interlayer temperature highly influences the quality of manufactured parts. This paper proposes an optimisation of deposition parameters for a better control of interlayer temperature while reducing the printing time employing a Finite Element (FE) model and a metamodel based on Non Uniform Rational Basis Splines (NURBS) entities for a thin-walled part in aluminium alloy. Firstly, the thermal FE model is created to extract the interlayer temperature as a function of different deposition parameters that will be optimised. These parameters are the wire feed speed and the cooling time between deposition of two consecutive layers. Then, a NURBS-based metamodel is generated to approximate the (unknown) transfer function between input variables and output responses of the problem at hand. One of the advantages of this metamodeling strategy is the possibility of obtaining the gradient of the output responses without the requirement of further computational resources, as the resulting metamodel is available in analytical form with the requisite continuity and differentiability. The NURBS-based metamodel is generated as a solution of a three-step optimisation strategy aiming at determining all the parameters defining the shape of the NURBS entity. Finally, the NURBS-based metamodel is included in the optimisation process related to the considered application. The optimisation problem is defined as a weighted sum of different criteria, i.e., total printing time and the average interlayer temperature difference for each layer. The solution obtained is subsequently validated a posteriori using the high-fidelity FE model, demonstrating an excellent agreement between the prediction of the NURBS-based metamodel and those of the FE model. [ABSTRACT FROM AUTHOR]

In a fuel cell, flow channels are crucial components responsible for various essential functions that enable the system to operate effectively. The design of a directly coupled flow channel in a Proton Exchange Membrane Fuel Cell (PEMFC) system, assuming deterministic parameters, has been extensively studied. However, this deterministic approach neglects the inherent uncertainties in system performance during real-life operation, resulting in potentially unreliable and suboptimal performance. To address this issue, we propose a reliability-based design optimization (RBDO) of the PEMFC's channel structure, considering uncertainties in operating parameters. This paper presents a numerical model of the PEMFC in COMSOL, deterministic designs, reliability-based designs and a global sensitivity analysis on the PEMFC cell's potential output and average water activity on the membrane. Although the RBDO approach shows a reduction in cell efficiency compared to the deterministic design, it significantly improves reliability, with increases from 60.92% to 95.10% for cell potential and from 79.31% to 96.85% for water activity. [ABSTRACT FROM AUTHOR]

Conventional approaches for designing metasurfaces with the desired scattering response are often a time‐consuming and onerous process that heavily relies on the design experience and empirical methods. In this paper, a time and resource‐efficient design method is proposed for the synthesis of multiple metasurface polarizers with an efficient automated data generation process by utilizing the structure validation surrogate model (SVSM). Moreover, a novel attention and space‐to‐depth enhanced adaptive tandem network (ASE‐ATN) is proposed which integrates symmetry‐aware space‐to‐depth (SA‐SPD) along with the attention mechanism for efficient feature extraction. Several design examples of metasurfaces with linear polarization to orthogonal linear polarization (LP‐OLP) and/or linear polarization to circular polarization (LP‐CP) conversion capabilities are provided to verify the performance of the proposed method. The feasibility of the proposed method is demonstrated through the fabrication and measurement of a dual‐band dual‐polarization converter. [ABSTRACT FROM AUTHOR]

The paper introduces the assessment model of effectiveness of surrogate model for predicting indoor air temperature in buildings using a range of machine learning techniques and optimized data sampling algorithms, with the primary objectives of enhancing prediction accuracy. The main aim is to improve the forecasting accuracy. Among the evaluated models, the SVM demonstrates the highest error rates, as evidenced by its higher mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) values. A negative R² value also indicates that the SVM model is performing poorly, unable to capture the underlying patterns in the data effectively. In contrast, linear regression performs significantly better, with lower error rates and an R² value of 0.871, explaining 87.1% of the variance in the data and indicating a strong linear relationship. Gradient Boosting demonstrates excellent performance, with low error rates and a high R² value of 0.992, which almost completely covers the variance of the data and provides highly accurate predictions. AdaBoost surpasses all other models by achieving the lowest errors. With an R² value of 0.998, it accounts for 99.8% of the data's variance, making it the most precise model for this dataset. Overall, AdaBoost exhibits the highest accuracy with the smallest error rates and the highest R² value, with Gradient Boosting coming in second. [ABSTRACT FROM AUTHOR]

We propose a set of equations for preliminary risk‐targeted seismic design of structures base‐isolated using single friction pendulum (SFP) bearings. The equations offer statistical estimates of the maximum displacement of the superstructure relative to the base and the maximum displacement of the isolated base response quantities of interest (RQIs), given ground motion intensity measures and the essential dynamic properties of the SFP bearing base‐isolated structure. The set of proposed equations enables preliminary seismic design of SFP base‐isolated structures using the mean annual frequency of exceeding displacement‐defined limit states related to the two RQIs. To develop the design equations, we define and use a two‐degree‐of‐freedom (2DOF) surrogate model that features a few essential dynamic parameters of the SFP bearing base‐isolated structure. We first verify that the 2DOF surrogate model is accurate enough to represent the base and superstructure displacements compared to the complete multiple‐degree‐of‐freedom model of the prototype SFP bearing base‐isolated structure. Next, we perform more than 5 million non‐linear dynamical analyses of 4900 distinct 2DOF surrogates subjected to 210 different recorded ground motions scaled five times. We fit the response data of each 2DOF surrogate to a linear model and use two different modeling techniques to derive the design equations. One model is based on Polynomial Chaos Expansion (PCE), and the other model is based on Linear Regression (LR). The PCE model is more accurate than the LR model, with the trade‐off regarding its simplicity. Nevertheless, both the PCE and the LR models are accurate enough to estimate the design quantities of interest of the SFP bearing base‐isolated structure for preliminary design purposes. Lastly, we exemplify the use of the proposed design equations and we show that the estimate of base displacement is conservative in the cases when the superstructure of the SPF bearing base‐isolated structure yields. [ABSTRACT FROM AUTHOR]

This paper introduces MTO-DNNCLS, a novel multiscale topology optimization (TMO) framework using dual neural networks and cutting level sets. It designs graded lattice structures with non-uniform microstructures, implicitly represented by level set functions combined from multiple prototypes. Non-uniformity is achieved by adjusting cutting height variables. Additionally, two neural networks are employed: one for neural reparameterization and another as a surrogate model. Specifically, the first neural network (NN) takes predefined point coordinates as input, outputting cutting height variables that determine the microstructure. Through neural reparameterization, the multiscale structure evolves iteratively based on the response analysis results and loss functions. Meanwhile, another neural network is utilized to construct a high-precision surrogate model between the cutting height variables to the elasticity tensor and volume fraction, reducing computational costs of real-time homogenization. The reparameterization process embedded with the surrogate model enables direct internal gradient propagation, eliminating the need for manual sensitivity derivation and significantly simplifying sensitivity analysis. Compliance minimization and shape matching examples are presented to demonstrate the effectiveness and robustness of the proposed framework. Finally, additive manufacturing (AM) and mechanical testing are employed to validate that the obtained non-uniform microstructure exhibits superior stiffness and strength compared to the uniform microstructure. [ABSTRACT FROM AUTHOR]

We introduce deep generative diffusion for multivariate and regional surrogate modeling learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the model is trained to generate forecasts for a 12‐hr lead time from simulations by the state‐of‐the‐art sea‐ice model neXtSIM. For our regional model setup, the diffusion model outperforms as ensemble forecast all other tested models, including a free‐drift model and a stochastic extension of a deterministic data‐driven surrogate model. The diffusion model additionally retains information at all scales, resolving smoothing issues of deterministic models. Furthermore, by generating physically consistent forecasts, previously unseen for such kind of completely data‐driven surrogates, the model can almost match the scaling properties of neXtSIM, as similarly deduced from sea‐ice observations. With these results, we provide a strong indication that diffusion models can achieve similar results as traditional geophysical models with the significant advantage of being orders of magnitude faster and solely learned from data. Plain Language Summary: Thanks to generative deep learning, computers can generate images that are almost indistinguishable from real images. We use this technology to forecast the sea‐ice with models that are solely learned from data, here from simulation data. Doing so for a region North of Svalbard, we enhance the accuracy of the model and maintain their sharpness. The learned model further depicts physical processes as similarly observed for the targeted physical‐driven model. Therefore, this technology could provide us with the necessary tools to learn faster models from data that have similar properties to those based on physical equations. Key Points: We introduce the first denoising diffusion model designed for sea‐ice physicsGenerative diffusion outperforms deterministic surrogates and retains the sharpness in the forecasts as observed in the targeted simulationsOur model generates forecasts that exhibit physical consistency between variables in space and time [ABSTRACT FROM AUTHOR]

CFRP is characterized by its anisotropic properties. Therefore, further structural weight reduction might be achieved by introducing the fiber direction as a design variable. For this study, steering fiber laminates defined by a Bezier curve are applied to a skin panel of CFRP stiffened panels. Then, simultaneous optimization of the fiber paths and stringer dimensions is performed to minimize the panel weight. We propose a new surrogate model that combines two neural networks and Latin hypercube sampling. Stiffness, manufacturability, and buckling load are introduced as constraints. Also, optimization is performed using the surrogate model and a genetic algorithm (GA). Furthermore, the stringer dimensions and fiber directions are optimized for straight fiber laminates. Comparison of steering fiber laminates and straight fiber laminates demonstrated that the introduction of steering layup into skin plates can reduce the stringer weight by up to 25.6%. An effective method for designing the geometric dimensions and layup path is provided. [ABSTRACT FROM AUTHOR]

This paper considers the use of surrogate-based analysis and optimization (SBAO) methods to investigate the performance of pulsed jet actuators for active separation control in a wind tunnel. Two experimental setups are examined: pressure-induced separation on a one-sided diffuser and trailing-edge separation on a NACA 64A-015 airfoil. In both cases the modeling is done using Gaussian process regression (kriging), and the investigated active-flow-control parameters are the amplitude, frequency, and duty cycle of the actuators that are used to mitigate boundary-layer separation. In the diffuser test case, a parameter-space exploration is conducted to examine the effect of the three input parameters on the amount of reverse flow detected by an array of calorimetric shear-stress sensors. In the airfoil test case, an optimization strategy is followed to maximize an objective function constructed with the airfoil sectional lift coefficient and the mass flow consumption of the actuators. Both experiments consistently indicate that lowering the duty cycle of the pulsed-jet actuators below 0.5 may lead to efficiency gains in active separation control by limiting their mass flow consumption for equal performance, but with a concomitant supply pressure increase. Overall, the results presented herein demonstrate that SBAO methods could provide a potential for more efficient wind tunnel investigations involving multiparameter problems. [ABSTRACT FROM AUTHOR]

Bi-fidelity surrogate (BFS) modeling is a powerful technique to mitigate the constraints of computational time and resources in high-fidelity (HF) models. However, obtaining sufficient HF samples in real-world scenarios remains challenging. This issue drives the creation of dependable surrogates capable of performing effectively on limited datasets. In this research, an innovative method is devised for constructing a BFS model based on scaled correlation construction and penalty minimization, with the goal of improving the performance of surrogate models when data are limited. This method starts by establishing a tuning factor that measures the contribution of different features to the response, thereby reducing the impact of redundant features on model construction. This factor is integrated into the construction of the discrepancy function to represent the variations between low-fidelity (LF) and HF samples. Additionally, a regularization constraint is imposed on the model parameter to prevent overfitting, which in turn increases the model’s robustness and interpretability. To validate the superiority of the developed BFS model, five leading surrogate models are selected for comparison. Experiments are conducted across various dimensions and nonlinearities of numerical problems, showcasing the competitiveness of the developed BFS model. Furthermore, a case study in engineering illustrates the practical application of the developed BFS model in the real-world scenarios. [ABSTRACT FROM AUTHOR]

Auxetic metamaterials, characterized by their negative Poisson's ratio, offer promising prospects for utilization in absorbing energy during quasi-static compressive loading as well as in applications requiring impact energy absorption. The optimization of auxetic structures' geometrical parameters can improve their performance. This research aims to optimize the design of an auxetic structure for maximum specific energy absorption and investigate its behavior under quasi-static compressive and high-velocity impact loading. The geometrical parameters of the cross-petal auxetic structure are optimized using genetic algorithm and a neural network surrogate model. The behavior of the optimized auxetic structure is examined in quasi-static compressive loading and compared with that of the basic auxetic structure using finite element simulations. The optimized auxetic structure is then evaluated in high-velocity impact loading as the core of a sandwich panel, with two plates placed in the front and rear. Simulations of projectile impacts at velocities ranging from 100 to 250 m/s reveal the sandwich panel's behavior. Results indicate a 69.82% increase in specific energy absorption capacity for the optimized auxetic structure as compared to the basic structure in quasi-static compressive loading. In high-velocity impact, the sandwich panel with the optimal auxetic core outperforms the one with the basic core. At velocities more than the minimum perforation velocity, the core contributes about 64%–67% of the total absorbed energy by the sandwich panel. [ABSTRACT FROM AUTHOR]

Boundary conditions play a crucial role in groundwater contamination source identification (GCSI), but they may be complex and reliable estimates are difficult to obtain in advance in actual situations. If the estimated values deviate significantly from the actual situation, the GCSI results will be inaccurate. However, very few studies have attempted to identify the boundary conditions in GCSI, and even when they are identified, they are often considered too simple. The boundary mode (Bmode) is assumed to be known, but in reality, it is often unknown and is more complex than initially assumed. Previous practices based on this assumption may not accurately reflect actual situations. Therefore, this study focused on the concentration boundaries, and the boundary conditions were also considered unknown variables, along with contamination source information and model parameters. To alleviate the problem of identifying the boundary conditions under an unknown Bmode, we proposed for the first time to treat the Bmode as an unknown variable. Thus, the source information, model parameters, Bmode, and corresponding parameters in the boundary concentration (BC) function were identified simultaneously. The Differential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) algorithm and a Kriging surrogate model were used as the primary means of solution. We designed four different synthetic cases to test the effectiveness of the above ideas. When identifying the Bmode, the obtained BC mostly fitted well with the true BC. It was therefore considered feasible for identifying the Bmode. The performance of the DREAM(ZS) algorithm was found to be superior to the traditional DREAM algorithm and was more efficient. [ABSTRACT FROM AUTHOR]

Studies on aerodynamic controls of central-slotted box decks primarily focused on mitigating vortex-induced vibrations (VIV), as this type of deck typically performs well against flutter instability. However, as the span length increases, the critical wind speed of aerodynamic static instability (U cr ) might be lower than flutter critical wind speed. Thus, U cr will determine the overall aerodynamic performance of such bridges. Investigating this instability through wind tunnel testing methods and numerical simulation can be expensive and time-consuming. In this paper, surrogate models using machine learning approaches, specifically artificial neural network (ANN) and extreme gradient boosting (XGBoost), were developed and optimized for fast and reliable prediction for U cr based on wind tunnel tests and simulation data. The results demonstrated that the built surrogate models can predict U cr accurately. The parametric study results showed that the height ratio of wind fairing apex (a/b), wind angle of attack (α), and length of the main span (L) have the most influence on the U cr compared with other parameters. Finally, based on the developed ANN surrogate model and the artificial bee colony (ABC) optimization algorithm, an optimized section was proposed to enhance the section's performance against aerodynamic static instability. [ABSTRACT FROM AUTHOR]

This work proposes an accurate and efficient surrogate modeling method for predicting combustion field in a gas-turbine combustor. The method integrates proper orthogonal decomposition-based dimensional reduction, and Gaussian process regression, in conjunction with the similarity-based sample processing technique. The design parameters of concern include fuel mass flowrate and swirler vane angle. Global surrogate models (GSMs) based on proper orthogonal decomposition and kriging produce significant errors for spatial emulation of methane concentration and turbulent kinetic energy (TKE), which is found to be largely attributed to the feature disparity of sample data at different design points. The Tanimoto coefficient is introduced to identify the similarity relation of the sample design points. The similarity-based sample processing method leverages the techniques of radial partitioning, azimuthal rotation, and sample similarity clustering to enhance the similarity among samples. The radial partitioning divides the physical fields into subzones according to the peak and trough characteristics along the radial direction. Local surrogate models (LSMs) are then adaptively constructed in the subzones, through azimuthal rotation for the methane concentration field and sample similarity clustering for the TKE field. The results show that the LSMs reduce the average prediction error of the CH4 concentration field from 19.56% to 8.16% and the TKE field from 93.75% to 9.12% compared to the GSMs. The present method can effectively support the surrogate modeling of combustors with complex variations of geometric structures and flow physics. [ABSTRACT FROM AUTHOR]

Dust removal in ports exacerbates water shortages and coastal pollution, particularly in coal ports with significant dust production. Constructing green ports for water conservation and emissions reduction is the future direction for port development. Stormwater harvesting, especially through low-impact development (LID), emerges as an attractive solution in line with green ports. concept. However, determining optimal LID layouts is complex due to multiple objectives. Fluctuations in runoff coefficients, stemming from changing underlying surface types in coal ports, are often overlooked, resulting in costly and ineffective LID layouts that fail to adequately control runoff across varying scenarios. This study innovatively addresses the impact of underlying surface stochasticity on optimizing LID layouts in coal ports. First, the storm water management model (SWMM) is employed to simulate runoff changes in coal ports under various representative underlying surface scenarios, generated through the K-Medoids method. The analysis reveals a significant 70.6% variation in the stockyard's total runoff during a 1-year 2-h design rainfall, ranging from 14,100 m3 to 48,000 m3. Subsequently, a multiobjective stochastic programming model for LID layout optimization is proposed, coupled with a surrogate model for SWMM. Two objectives for LID layout optimization are considered: investments and total runoff harvesting. Finally, the Nash bargaining solution is applied to balance the trade-off between the two objectives and obtain the optimal LID layout considering underlying surface stochasticity. Results indicate that the optimal LID layout has a cost of 1.636 billion Chinese yuan (CNY) and achieves a 50.98% runoff harvesting rate. Compared to previous studies ignoring underlying surface stochasticity, it demonstrates a 2% improvement in harvesting rate, a cost reduction of 200 million CNY, and shows higher robustness with 96 compliance instances out of 100 simulations. This study offers methodological support for developing economically efficient planning and construction schemes for stormwater harvesting facilities in coal ports. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space, fully considering geological uncertainty and the characteristics of reservoirs performance under time-varying well control conditions, creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks (CE-GAN). The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions, and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution, initial reservoir performance and time-varying well control, thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions. The experimental results in basic (egg model) and actual water-flooding reservoirs show that the model predictions align well with numerical simulations. In the basic reservoir model validation, the median relative residuals for pressure and oil saturation are 0.5% and 9.0%, respectively. In the actual reservoir model validation, the median relative residuals for both pressure and oil saturation are 4.0%. Regarding time efficiency, the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models, respectively, compared with traditional numerical simulations. The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization. [ABSTRACT FROM AUTHOR]

The application of optimal control theory in practical engineering is often limited by the modeling cost and complexity of the mathematical model of the controlled plant, and various constraints. To bridge the gap between the theory and practice, this paper proposes a model-free direct method based on the sequential sampling and updating of surrogate model, and extends the ability of direct method to solve model-free optimal control problems with general constraints. The algorithm selects sample points from the current actual trajectory data to update the surrogate model of controlled plant, and solve the optimal control problem of the constantly refined surrogate model until the result converges. The presented initial and subsequent sampling strategies eliminate the dependence on the model. Furthermore, the new stopping criteria ensure the overlap of final actual and planned trajectories. The several examples illustrate that the presented algorithm can obtain constrained solutions with greater accuracy and require fewer sample data. • The proposed sampling strategies do not depend on the models of controlled plant. • New stopping criteria ensure the overlap of final actual and planned trajectories. • Model-free direct method can deal with constrained optimal control problems well. [ABSTRACT FROM AUTHOR]

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This work introduces a new adding points strategy for augmenting the accuracy of reservoir proxy model and improving the effect of well control optimization. The method is based on the optimization process of a radial basis function neural network and genetic algorithm (GA), which aids in identifying the more important points to be included in the sample space. Notably, the uniqueness of this method lies in selecting the points of higher importance for subsequent optimization processes across the entire sample space. These selected points are then added to the surrogate model. The surrogate model is updated for each generation until the termination condition is satisfied, enabling the surrogate model to achieve improved accuracy. The results show that the new method is more effective, superior, and converges faster than the traditional method. [ABSTRACT FROM AUTHOR]

To satisfy the requirements of individual design and rapid performance evaluation of complex products, this paper proposes a hybrid approach to build a performance evaluation model and perform the rapid evaluation of design schemes. This approach consists of a surrogate model and knowledge graph (KG). Firstly, the KG of complex electromechanical products is established by Web Ontology Language to provide information about parts and evaluation indexes for the sampling process. It includes building ontology and writing inference and query rules at the framework level. Secondly, based on the sample points, a dynamics model is built and used for simulation. Using the Design of Experiments, the variables that have the greatest impact are found. The relevant variables will be input into the model to obtain the data set. According to the data set, a surrogate model based on the radial basis function is built as a performance evaluation model, which can improve computing efficiency to achieve evaluation results rapidly. In this study, the bogie design is used as a test case to evaluate the proposed method. And the results show that it can improve design efficiency for design issues such as part selection. [ABSTRACT FROM AUTHOR]

As the development of floating offshore wind turbines (FOWTs) progresses from offshore to deeper sea, the demands on mooring systems to ensure the safety of the structure have become increasingly stringent, leading to a concomitant rise in costs. A parameter optimization method for the mooring system of FOWTs is proposed, with the mooring line length and anchor radial spacing as the optimization variables, and the minimization of surge, yaw, and nacelle acceleration as the objectives. A series of mooring system configuration samples are generated by the fully analytical factorial design method, and the open source program OpenFAST is employed to simulate the global responses in the time domain. To enhance the efficiency of the optimization process, a multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), is utilized to find the Pareto-optimal solutions, alongside a Kriging model, which serves as a surrogate model for the FOWTs. This approach was applied to an IEC 15MW FOWT to demonstrate the optimization procedure. The results indicate that the integration of the genetic algorithm and the surrogate model achieved rapid convergence and high accuracy. Through this optimization process, the longitudinal motion response of FOWTs is reduced by a maximum of 6.46%, the yaw motion by 2.87%, and the nacelle acceleration by 11.55%. [ABSTRACT FROM AUTHOR]

Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slow response times for traditional algorithms. Surrogate model expensive multi-objective evolutionary algorithms (SM-EMOEAs), which are computationally efficient and converge quickly, are effective methods for the solution of MSIMP. However, the recent advances in this field have not been comprehensively summarized; therefore, this work provides a comprehensive overview of this subject. Firstly, the basic classification of MSIMP and its different fields of application are introduced, and the constraints of MSIMP are comprehensively analyzed. Secondly, the MSIMP problem is described to clarify the application scenarios of traditional optimization algorithms in MSIMP and their properties. Thirdly, the process of MSIMP and the classical expensive multi-objective evolutionary algorithms are reviewed to explore the surrogate model and the expensive multi-objective evolutionary algorithms based on MSIMP. Fourthly, improved SM-EMOEAs for MSIMP are analyzed in depth in terms of improved surrogate models, adaptive strategies, and diversity maintenance and quality assessment of the solutions. Finally, SM-EMOEAs and SM-EMOEA-based MSIMP are analyzed in terms of the existing literature, and future trends and directions are summarized. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II (Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II) for solving complex multiobjective optimization problems, with a particular focus on robotic leg-linkage design. The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II, aiming to enhance the efficiency and precision of the optimization process. Through a series of empirical experiments and algorithmic analyses, the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods, underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands. The methodology encompasses a detailed exploration of the algorithm’s configuration, the experimental setup, and the criteria for performance evaluation, ensuring the reproducibility of results and facilitating future advancements in the field. The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization. By bridging the gap between complex optimization challenges and achievable solutions, this research contributes valuable insights into the optimization domain, offering a promising direction for future inquiries and technological innovations. [ABSTRACT FROM AUTHOR]

Due to the inherent unpredictability of marine trial conditions, safety issues in the launching and recovery process are commonplace. For these issues, digital twin (DT) maintenance offers a novel idea. In this paper, a blended-wing-body underwater glider skeleton is selected as a research object. And a simple demo is created to investigate the major technologies that may be involved in DT maintenance. According to this specimen, the necessary development environments are developed to obtain physical information and produce a DT model in cyberspace respectively. An attitude sensor is used as the physical data collector, while real-time structural field prediction and virtual reality visualisation are employed. By using the attitude angles and structural data, it is possible to achieve real-time monitoring of the skeleton strength. Through the means of this straightforward case study, the key technologies are supported and can be applied to far more complex inquiries. [ABSTRACT FROM AUTHOR]

Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety assessments. Current methods for determining slider peeling strength are primarily physical testing and numerical simulation. However, these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately addressed. Therefore, the efficient and low-cost surrogate model emerges as a promising solution. Nevertheless, currently used surrogate models suffer from inefficiencies and complexity in data sampling, lack of robustness in local model predictions, and isolation between data sampling and model prediction. To overcome these challenges, this paper aims to set up a systematic framework for slider track peeling strength prediction, including sensitivity analysis, dataset sampling, and model prediction. Specifically, the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling strength. Based on the variable sensitivity, a distance metric is constructed to measure the disparity of different variable groups. Then, the sparsity-targeted sampling (STS) is proposed to formulate a representative dataset. Finally, the sequentially selected local weighted linear regression (SLWLR) is designed to achieve accurate track peeling strength prediction. Additionally, a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a mediator. Experimental results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization robustness. Furthermore, the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements, achieving an average absolute error of 3.3 kN in the simulated test dataset. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck's energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency. [ABSTRACT FROM AUTHOR]

Composite materials offer the unique advantage of allowing customization of their properties based on their load. However, the optimization of composite laminate properties can often be challenging, often leading to quasi-isotropic designs or the use of industry guidelines. This paper presents a novel method for optimizing of a composite cylinder under axial compression. It introduces an innovative approach by merging a tailored differential evolution algorithm with a deep neural network. The key modification is in the method of constraint implementation. The initial population and trial vectors are constrained to balanced laminates using a while loop, effectively shrinking the design space and reducing computational requirements. The advantage of the customization is reflected in the faster convergence of the optimization as well as a much more accurate deep neural network model. It also enabled the differential evolution to escape the local maxima. Using the deep neural network to evaluate candidate solutions, further reduces the computational costs. The technique is validated using linear buckling analysis and applied to design an inter-tank truss structure. The optimization resulted in a drop in the mass of the truss structure from 5.28 to 4.87 kg. The study establishes a general optimization method applicable to various composite cylinders, including short and long, thin and thick cylinders, and honeycomb core sandwiched composite structures. [ABSTRACT FROM AUTHOR]