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Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R2) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R2 values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.

Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus field trials, located in Highlands County and in Polk County, Florida, USA. In each location, trials contained either ‘Hamlin’ or ‘Valencia’ sweet orange scion grafted on more than 30 different rootstocks. Leaves were collected and analyzed in the laboratory to determine macro- and micronutrient concentration using traditional chemical methods. Spectral data from tree canopies were obtained in five different bands (red, green, blue, red edge and near-infrared wavelengths) using a UAV equipped with a multispectral camera. The estimation model was developed using a gradient boosting regression tree and evaluated using several metrics including mean absolute percentage error (MAPE), root mean square error, MAPE-coefficient of variance (CV) ratio and difference plot. This novel model determined macronutrients (nitrogen, phosphorus, potassium, magnesium, calcium and sulfur) with high precision (less than 9% and 17% average error for the ‘Hamlin’ and ‘Valencia’ trials, respectively) and micro-nutrients with moderate precision (less than 16% and 30% average error for ‘Hamlin’ and ‘Valencia’ trials, respectively). Overall, this UAV- and AI-based methodology was efficient to determine nutrient concentrations and generate nutrient maps in commercial citrus orchards and could be applied to other crop species.

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB.

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.

Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils.

This study evaluates the future climate fluctuations in Iran’s eight major climate regions (G1–G8). Synoptic data for the period 1995–2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020–2040), middle future (2040–2060), and far future (2060–2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson’s correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995–2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by −74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by −4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m−2 (38.38%). These values are 0.64 and 42.97 W·m−2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R2 value of 0.92 and an RMSE of 15.40 W·m−2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

Thin-walled parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, and residual stress. In the paper, a new approach for quantifying the influence of initial residual stress on machining distortion uncertainty is proposed by combining the gradient boosting regression tree algorithm and finite element simulation. Firstly, finite element simulation is used to model the milling process of the workpiece and obtain machining distortion data. These data are validated using small deflection theory of thin plates. Next, the machining distortion simulation data is expanded using a gradient boosting regression tree to learn the coupling influences between material removal rate, inherent uncertainty in initial residual stresses, and machining distortion uncertainty. Finally, based on the predictive data from the gradient boosting regression tree, the machining distortion uncertainty inference is conducted, indirectly quantifying the influence of redistribution of initial residual stresses and the uneven distribution of these stresses on machining distortion uncertainty. Among them, the influential weight of the initial residual stress redistribution on the machining distortion uncertainty is 0.903, and the influential weight of uneven distribution of these stresses on the machining distortion uncertainty is 0.097. Taking the web structure and T-shaped thin-walled parts as examples, the measures to reduce the influence of initial residual stress redistribution on the machining distortion uncertainty are investigated. It is found that the machining distortion uncertainty caused by the strategy of spiral milling from inwards to outwards is the smallest, only 0.012. Furthermore, in addition to strictly controlling the finishing allowance, the finishing allowance should be symmetrically distributed near the neutral layer as far as possible.