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This paper presents novel contributions to both theory and solution methodology in AI-based analysis of solid mechanics. The physics-informed neural network (PINN) method is developed for thermoelastic wave propagation and Moore-Gibson-Thompson (MGT) coupled thermoelasticity analysis of porous media, a first in the field. The coupled thermoelasticity governing equations, based on the MGT heat conduction model, are derived for a porous half-space, with the thermal relaxation coefficient and strain relaxation factor being considered. Mechanical and thermal shock loading boundary conditions are imposed. The behavior of a magnesium-made porous body is analyzed using the PINN method, with highly accurate results being achieved for the system of coupled PDEs. An adaptive hyperparameter tuning approach, integrating a generalized subset design (GSD) and Bayesian optimization algorithm, is used to automatically select the optimal structure based on the L2 relative error. This hybrid methodology eliminates manual adjustment concerns. The proposed method is verified through a thorough comparison with the Lord-Shulman theory of coupled thermoelasticity. The strength of the methodology lies in its ability to operate without domain data, with only boundary and initial points being required. Four example sets are examined to demonstrate the capabilities of the modified PINN, and high-quality predictions of dimensionless fields' variables over an extended time interval are obtained, confirming its extrapolation abilities. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) acquisition is vital for crop stress diagnosis and precision irrigation. However, UAV remote sensing-based SMC monitoring usually suffers from low accuracy and spatio-temporal applicability. To address these issues, this study integrated crop physiological spectral response features into an ensemble learning model. A two-year field experiment (2022–2023) was conducted. First, fractional-order differentiation (FOD) and continuous wavelet transform (CWT) were used to enhance the responsiveness of summer maize canopy spectra to SMC and leaf physiological parameters (LPPs), including leaf area index (LAI), leaf chlorophyll content (LCC) and leaf water content (LWC). Afterwards, variable importance in projection (VIP) was adopted to characterize the spectral response properties of each parameter. Finally, a stacked ensemble learning model (SELM) based on Bayesian optimization (BO) was used to construct a SMC monitoring model, and the feasibility of fusing LPP spectral response features for SMC monitoring was evaluated. The results indicated that: (1) Changes in SMC had significant effect on LCC and LAI of summer maize, which in T4 (with field water-holding capacity of 80 %-95 %) were 14.07 % and 34.41 % higher than that in T1 (40 %-50 %), respectively. (2) Spectral transformation could significantly enhance the correlation between SMC and LPPs with canopy spectra (the average increase of R reached 0.23). (3) Consideration of the crop physiology spectral response could improve the SMC monitoring accuracy, the LCC-FOD-BO-SELM had excellent monitoring performance (R²= 0.78; RMSE = 0.019). The monitoring model fusing LPPs spectral response features had the highest accuracy (R²= 0.81; RMSE = 0.016). (4) BO could significantly reduce the overfitting problem of the monitoring model, with the maximum difference between the R2 of the training and test set after BO being only 0.01), thus improving the model's generalizability. This new approach to SMC monitoring using UAV hyperspectral data provide scientific support for precision agriculture and irrigation. [Display omitted] • FOD and CWT methods significantly enhance spectral characteristics. • LPPs spectral response features significantly improve SMC monitoring performance. • Bayesian optimization greatly improves the robustness of SELM [ABSTRACT FROM AUTHOR]

Nanoparticle-enhanced lubricants (nanolubricants) achieve tribological properties that can signficantly outperform the unmodified base oil. However, this performance enhancement is known to be extremely senstive to the concentration of nanoparticles used. Here, we introduce a new approach to optimizing nanolubricants combining design of experiments (DOE) with machine learning, addressing the challenge of determining optimal nanoparticle concentration in base oils and demonstrating significant performance improvements. Combining Box-Behnken design with Bayesian optimization, we identify an optimal concentration of 0.04 wt% copper oxide (CuO) nanoparticles in a standard engine oil (SAE 20W50), resulting in a 42% reduction in coefficient of friction (COF) relative to the base oil. Surface analysis (AFM, SEM) confirmed improvement of anti-wear and anti-friction characteristics. Signficant enhacements of thermophysical properties were observed (demonstrated by increased flash and pour points, by 10% and 7% respectively), signifying greater thermal stability. The dynamic viscosity remained consistent, with a beneficial reduction observed under conditions of high temperature and shear, which aligns with the demands of engine lubricant standards for PC-12B oils, with a reduction from 3.49 to 3.18 cP (8%). This research introduces a novel hybrid optimization technique that provides a versatile framework for optimizing nanofluids, addressing the critical challenge of determining optimal nanoparticle concentration. [Display omitted] • A hybrid optimization strategy was developed to optimize the concentration of CuO nanoparticles in nanolubricants. • The optimized nanolubricant demonstrated an impressive 42% reduction in the coefficient of friction (COF). • AFM surface analysis confirmed enhancements in anti-wear and anti-friction properties. • Enhanced thermophysical properties underscored the positive impact of the hybrid optimization tool. • Dynamic viscosity remained stable, improving under high temperature and shear conditions in line with PC-12B oil standards. [ABSTRACT FROM AUTHOR]

Tunnel transportation is an integral part of modern traffic and holds significant importance in ensuring public safety and smooth traffic flow. To inspect the defects of tunnel linings, multiple line-scan cameras(LSCs) and a Laser Range Finder (LRF) are employed simultaneously to acquire the two-dimensional (2-D) textural images and three-dimensional (3-D) geometrical point cloud. Due to the limited view of LSCs, multiple 2-D images of the tunnel linings need to be stitched together to form a panoramic tunnel view. However, tunnel images often have few features, uneven lighting, and significant distortion around, making traditional feature-based image stitching inaccurate. In this study, we propose stitching the 2-D LSC images from the perspective of 3-D geometrical point cloud, which can also readily fuse the 2-D and 3-D information of tunnel linings. Firstly, a calibration target is carefully designed to coarsely calibrate the transformation parameters between the LRF and LSCs. Then, using Bayesian optimization, the parameters are iteratively refined by maximizing the similarity of overlapping LSC regions of common 3-D points. Finally, overlapping LSC images are adaptively blended with the graph cut to avoid breaking the details of tunnel linings while stitching LSC images together. Experimental results using both real tunnel and simulated data validate the effectiveness of the proposed tunnel linings inspection method. The dataset and code will be available at https://github.com/YuchunHuang/Tunnel_Inspection. [ABSTRACT FROM AUTHOR]

• For dc fault diagnosis in MMC-MT-HVdc grids, improved features are collected using DWT-based MRA and Parseval's theorem. • An optimal ANN architecture is used to classify fault patterns based on transient features. The proposed protection scheme is robust against severe noise conditions and high impedance faults. Bayesian optimization is a well-suited technique to optimize ANN hyperparameters. • The Bayesian optimization method also significantly reduces the time required to evaluate and select ANN models. • It is further finding an efficient autonomous solution to tune excessive multiple hyperparameters with the help of Bayesian optimization and providing a basic understanding of it. • This algorithm is implemented in a single-end main and coordinated secondary unit containing control logic. By detecting internal dc faults as soon as possible, the proposed scheme covers the failure of the main unit with expedited backup. This paper proposes a fault detection and classification scheme for multi-terminal high voltage direct current (MT-HVdc) lines by integrating discrete wavelet transform (DWT) multi-resolution analysis with artificial neural networks (ANNs). Previously, such intelligent protection schemes used manual approaches or arbitrary rules of thumb to optimize a set of hyperparameters of the neural networks without applying any optimization algorithm. In order to improve accuracy, this work proposes an efficient Bayesian Optimization (BO) approach for evaluating and establishing the regulated hyperparameters for ANNs. The DWT multi-resolution analysis (MRA) and Parseval's theorem are used to extract energy variation for various faults. The energy variation of fault signals at different scales is fed into a multi-stage model to optimize the hyperparameters of neural networks with minimal training setup time and compute effort. After training, the data-based algorithm is implemented in a single-end main and coordinated secondary unit with control logic. The proposed scheme intends to detect internal short-circuit dc faults as quickly as possible and cover the failure of the main unit with expedited backup action. The findings of the study reveal that the proposed scheme can accurately detect internal faults in a variety of testing conditions and remain stable against external faults or disturbances with an average recognition accuracy of 99.38%. [ABSTRACT FROM AUTHOR]

The WRF model cannot produce an accurate wind speed prediction to guarantee the steady operation of wind farms due to the inability of wind farms to acquire meteorological information and imperfections in physical schemes. This paper suggested an ultra-short-term wind speed correction method based on wind speed fluctuation characteristics, known as VSDA-BO-LSTM, in order to increase the accuracy of wind speed prediction from WRF model. First, the Volatile Stable Day model (VSDA), which separated the original wind speed data into segments, extracted fluctuation features, and categorized them, was proposed. This model successfully addressed the lack of common wind speed feature extraction and wind speed feature stripping. Then, the long-short-term memory (LSTM) deep learning algorithm combined with the output of the VSDA model was used to correct the wind speed prediction value of the WRF model. At the same time, the Bayesian optimization algorithm was used to determine the parameters of LSTM.The results of the experiment demonstrated the suggested method's good capability for wind speed correction. Moreover, the proposed method was very competitive compared to the current state-of-the-art model. • Wind speed feature is reflected in future wind speed by its correlation. • VSDA model can retain wind speed's randomness and extract its features. • The proposed method has been tested by robustness test and ablation experiment. [ABSTRACT FROM AUTHOR]

The goal of this paper is to develop a modeling framework that captures the inter-decision dynamics between mobility service providers (MSPs) and travelers that can be used to optimize and analyze policies/regulations related to MSPs. To meet this goal, the paper proposes a tri-level mathematical programming model with a public-sector decision maker (i.e. a policymaker/regulator) at the highest level, the MSP in the middle level, and travelers at the lowest level. The public-sector decision maker aims to maximize social welfare via implementing regulations, policies, plans, transit service designs, etc. The MSP aims to maximize profit by adjusting its service designs. Travelers aim to maximize utility by changing their modes and routes. The travelers' decisions depend on the regulator and MSP's decisions while the MSP decisions themselves depend on the regulator's decisions. To solve the tri-level mathematical program, the study employs Bayesian optimization (BO) within a simulation–optimization solution approach. At the lowest level, the solution approach includes an agent-based transportation system simulation model to capture travelers' behavior subject to specific decisions made by the regulator and MSP. At the middle and highest levels, the solution approach employs BO for the MSP to maximize profit and for the regulator to maximize social welfare. The agent-based transportation simulation model includes a mode choice model, a road network, a transit network, and an MSP providing automated mobility-on-demand (AMOD) service with shared rides. The modeling and solution approaches are applied to Munich, Germany in order to validate the model. The case study investigates the tolls and parking costs the city administration should set, as well as changes in the public transport budget and a limitation of the AMOD fleet size. Best policy settings are derived for two social welfare definitions, in both of which the AMOD fleet size is not regulated as the shared-ride AMOD service provides significant value to travelers in Munich. • Optimization of regulatory measures for autonomous mobility on-demand systems. • Hierarchical decision-making of regulator, mobility service provider and travelers. • Solution approach using multi-level, multi-objective Bayesian optimization. • Agent-based model including ridepooling, private vehicles and public transport. • Fleet size should not be regulated; toll and parking fees depend on scenario. [ABSTRACT FROM AUTHOR]