Search Results (120)

Previous research has primarily focused on assessing seasonal mean or annual extreme climate events, whereas intraseasonal variability in extreme climate has received comparatively little attention, despite its importance for understanding short-term climate dynamics and associated risks. This study evaluates the performance of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing summer maximum temperature (Tmax) variability across China during 1979–2014, with the variability defined as the standard deviation of daily Tmax anomalies for each summer. Results show that most CMIP6 models fail to reproduce the observed north–south gradient of Tmax variability with significant regional biases and limited agreement on temporal trends. The multi-model ensemble (MME) outperforms most individual models in terms of root-mean-square error and spatial correlation, but it still under-represents the observed temporal trends, especially over southeastern and central China. Taylor diagram analysis reveals that EC-Earth3, GISS-E2-1-G, IPSL-CM6A-LR, and the MME perform relatively well in capturing the spatial characteristics of Tmax variability, whereas MIROC6 shows the poorest performance. These findings highlight the persistent limitations in simulating intraseasonal Tmax variability and underscore the need for improved model representations of regional climate dynamics over China. Full article

This study aimed to identify the most reliable prediction model for estimating the compressive strength of concrete by conducting a comparative analysis of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) methodologies. The modeling process utilized 92 experimental data points for training purposes and allocated 28 data points for testing validation. PSO was employed to optimize coefficients within mathematical equations used for concrete compressive strength prediction, facilitating the development of appropriate models based on various error metrics. Specifically, PSO models optimized to minimize Weighted Root Mean Square Error (WRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) criteria were evaluated against the highest-performing model developed using ANN. Model A, optimized using a constant term and the WRMSE loss function within a PSO-ANN framework, achieved the highest performance, with a correlation coefficient exceeding 0.99 and low error values on the training dataset. The same model also demonstrated strong predictive accuracy and low error on the test dataset, indicating excellent generalization capability. In contrast, the standalone ANN model exhibited near-perfect accuracy on the training data (R² = 0.9994) but suffered a significant drop in performance on the test data (correlation ≈ 0.60). This highlights the impact of overfitting and underscores the importance of regularization techniques for improving generalizability. Through comprehensive statistical and visual assessments using Taylor diagram analysis, PSO-based models demonstrated significantly superior accuracy compared to the ANN model. Furthermore, the constant-term WRMSE model exhibited optimal generalization performance and provided the most reliable predictions among all tested models. It has been observed that highly accurate predictions can be made even for values outside the range of the data used. The results obtained in this study indicate that reliable predictive models for concrete production can be developed using both the available data and information from the literature. In cases where data are lacking, it is also possible to establish these models by conducting a sufficient number of experiments. Full article

The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. Six thousand samples (one thousand per panel type) were generated using the Latin Hypercube Sampling method to ensure a diverse and comprehensive dataset. The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions. The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). Using regression metrics, performance was benchmarked against classical machine learning models such as CatBoost, XGBoost, LightGBM, random forest, decision tree, and k-nearest neighbors. The ANN achieved superior results: MSE = 2.9584, MAE = 0.9875, RMSE = 1.72, and R² = 0.9998, significantly outperforming all other models across all metrics. Finally, a Taylor Diagram was plotted to assess the model’s reliability and check for overfitting, further confirming the consistent performance of the ANN model across both training and testing datasets. These findings highlight the model’s potential as a robust and efficient tool for predicting the buckling strength of metallic aerospace grid-stiffened panels. Full article

The strengthening of reinforced concrete (RC) beams with aluminum alloy was typically implemented in a symmetrical configuration. To evaluate the flexural performance of strengthened beams, four machine learning (ML)-based models, namely Random Forest (RF), Xtreme Gradient Boosting (XGBoost), Adaptive Boosting (Adaboost), and Light Gradient Boosting Machine (LightGBM), were developed for predicting the flexural bearing capacity of aluminum-alloy-strengthened RC beams. A total of 124 experimental samples were collected from the literature to establish a database for the prediction models, with 70% and 30% of the data allocated as the training and testing sets, respectively. The K-fold cross-validation method and random search method were used to adjust the hyperparameters of the algorithm, thereby improving the performance of the models. The effectiveness of the models was evaluated through statistical indicators, including the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Additionally, absolute error boxplots and Taylor diagrams were used for statistical comparisons of the ML models. SHAP (Shapley Additive Explanations) was employed to analyze the importance of each input parameter in the predictive capability of the ML models and further examine the influence of feature variables on the model prediction results. The results showed that the predicted values of all models had a good correlation with the experimental values, especially the LightGBM model, which can effectively predict the flexural bearing capacity behavior of aluminum-alloy-strengthened RC beams. The research achievements provided a reliable prediction framework for optimizing aluminum-alloy-strengthened concrete structures and offered references for the design of future strengthened structures. Full article

Concrete carbonation is an important factor causing corrosion of steel reinforcement, which leads to damage to reinforced concrete structures. To address the problem of concrete carbonation depth prediction, this paper proposes a prediction model. The framework synergistically integrates Bagging and Boosting algorithms, specifically replacing the original Random Forest base learner with gradient Boosting variants (LightGBM (version 4.1.0), XGBoost (version 2.1.1), and CatBoost (version 1.2.5)). This hybrid approach exploits the strengths of all three algorithms to reduce variance and bias, and to further improve prediction accuracy, Bayesian optimization algorithms were used to fine-tune the hyperparameters, resulting in three hybrid-integrated models: Random Forest–LightGBM Fusion Framework, Random Forest–XGBoost Fusion Framework, and Random Forest–CatBoost Fusion Framework. These models were trained on a dataset containing 943 case sets and six input variables (FA, t, w/b, B, RH, and CO₂). The models were comprehensively evaluated using the comprehensive scoring formula and Taylor diagrams. The results showed that the hybrid-integrated model outperformed the single model, with the RF–CatBoost fusion framework having the highest test set performance (R² = 0.9674, MAE = 1.4199, RMSE = 2.0648, VAF = 96.78%). In addition, the Random Forest–CatBoost Fusion Framework identified exposure t and CO₂ concentration as the most important features. This paper demonstrates the applicability of a predictive model based on the Random Forest–CatBoost Fusion Framework in predicting the depth of concrete carbonation, providing valuable insights into the durability design of concrete. Full article

The control of rainfall extremes is essential in the design of hydro-agricultural works, as their performance depends on it. This study aims to determine the best-fit probability model suited to current climatic conditions in the Mono-Couffo basin in Benin. To this end, daily rainfall data from six rainfall stations from 1981 to 2021 were used. The application of the Decision Support System (DSS) with graphical and numerical performance criteria (such as RMSE, SD, and CC represented by the Taylor diagram; AIC and BIC) made it possible to identify the best distribution class and then to select the most suitable distribution for this basin. The results indicate that class C distributions, characterized by regular variations, are the most appropriate for the modeling maximum annual daily precipitation at all stations (78% of cases). Of these, the Inverse Gamma distribution proved to be the most suitable, although its estimation errors ranged from 16.47 mm/d at Aplahoué to 39.80 mm/d at Grand-Popo. The second most appropriate distribution is the Log-Pearson Type III. The use of the Inverse Gamma distribution is, therefore, recommended for hydro-agricultural development studies in the Mono-Couffo basin. Full article

With the impacts of climate change, floods have become increasingly frequent in recent years. Estimating flood hazard thresholds and peak floodwater levels based on flood frequency analysis is crucial for anticipating and preparing for potential flooding events. This study aims to estimate flood hazard thresholds, flood occurrence probabilities, and the return periods of peak floodwater levels in the Nokoue lake watershed in Benin. To achieve this, the standardized water level index, also known as the Flood hazard Index, was calculated to estimate flood hazard thresholds. The three best probability distribution models, Gumbel, Generalized Extreme Value (GEV), and Generalized Pareto (GPA), were selected to project future floodwater levels using annual maximum daily water level data for extreme floods from 1997 to 2022, obtained from a water gauge site at Nokoue lake. Three goodness-of-fit tests were applied to identify the best-fitting probability distribution model: a Taylor diagram (three-dimensional analysis), a cumulative probability density diagram based on the root-mean-square error (RMSE), and an L-moment diagram (two-dimensional analysis). The Flood hazard Index values ranged from −1.10 to +3.40, with 77.78% showing positive indices and 22.22% showing negative indices. The flood hazard thresholds were classified in ascending order of index values: limited hazards, moderate hazards, significant hazards, and critical hazards. The analysis results indicate that the flood hazard thresholds are defined as follows: below 3.94 m for limited hazards, from 3.94 m up to 4.04 m for moderate hazards, from 4.04 m to 4.14 m for significant hazards, and above 4.14 m for critical hazards. The distribution model analysis showed that the Gumbel distribution best fits the Nokoue lake watershed, with an RMSE of 0.0724, compared to 0.0754 and 0.0761 for the GEV and GPA models, respectively. The annual maximum daily water levels for various non-exhaustive return periods, 2, 3, 5, 10, 25, 50, and 100 years, were estimated and compared. The return period for the highest recorded annual maximum daily water levels (4.4 m/day) in the Nokoue lake watershed were calculated to be 12, 15, and 15 years using the Gumbel, GEV, and GPA models, respectively. Quantile analysis revealed that the Gumbel distribution produced overestimated results compared to the GEV and GPA models for return periods exceeding 10 years. Exceptional and very exceptional hydrological events have return periods of 100 and 150 years, corresponding to peak flow levels of 4.95 m and 5.05 m respectively. Finally, the results of this study will be invaluable for flood hazard managers in monitoring flood alerts and for water resource engineers in determining dimensions for designing flood control structures such as spillways, dams, and bridges, thereby improving the management of recurrent flooding events. Full article

Communication, collaboration, critical thinking, and creativity are core 21st century skills. Meanwhile, educational robotics is regarded as a contributor to their promotion. Hence, education tries to embrace them in school curricula. Yet, there is a lack of reviews in the existing literature presenting the robotics activities used to promote children’s communication, collaboration, creativity, and critical thinking from an early age. Consequently, this study employed a thematic literature review aiming to 1. map the research field of robotics activities suitable for promoting kindergarteners’ skills, 2. facilitate researchers and teachers in their current and future work related to robotics, and 3. provide guidelines and a model flow related to robotics activities for supporting educators in integrating them into their school reality. The PRISMA Flow Diagram and the Atlas.ti software were used to investigate the Scopus database and the Taylor and Francis register. Finally, 16 papers were examined out of 349 initially retrieved and published from 2014 to 2025. Based on our findings, a few interventions have been aimed at fostering kindergarteners’ communication, collaboration, creativity, and critical thinking via educational robotics, but there is rarely a clear record of robotics activities achieving that. Moreover, there is no specific model or guideline for developing such activities in kindergarten. Full article

Predicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships and variability. Machine learning has emerged as a powerful tool for optimizing and managing these processes. This study uses Bayesian optimization to tune parameters for various machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Elastic Net, Adaptive Boosting (AdaBoost), Gradient-Boosting Regressor (GBR), K-nearest Neighbors (KNN), and Decision Tree (DT), aiming to identify the best model for predicting the compositions of CO, CO₂, H₂, and CH₄ under different conditions. Performance was evaluated using the correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Relative Absolute Error (RAE), and execution time, with comparisons visualized using a Taylor diagram. Hyperparameter optimization’s significance was assessed via t-test effect size and Cohen’s d. XGBoost outperformed other models, achieving high R values under optimal conditions (0.951 for CO, 0.954 for CO₂, 0.981 for H₂, and 0.933 for CH₄) and maintaining robust performance under suboptimal conditions (0.889 for CO, 0.858 for CO₂, 0.941 for H₂, and 0.856 for CH₄). In contrast, K-nearest Neighbors (KNN) and Elastic Net showed the poorest performance and stability. This study underscores the importance of hyperparameter optimization in enhancing model performance and demonstrates XGBoost’s superior accuracy and robustness, providing a valuable framework for applying machine learning to energy management and environmental monitoring. Full article

Accurate wind speed and direction data are vital for coastal engineering, renewable energy, and climate resilience, particularly in regions with sparse observational datasets. This study evaluates the ERA5 reanalysis model’s performance in predicting wind speeds and directions at ten coastal and offshore stations in Kuwait from 2010 to 2017. This analysis reveals that ERA5 effectively captures general wind speed patterns, with offshore stations demonstrating stronger correlations (up to 0.85) and higher Perkins Skill Score (PSS) values (up to 0.94). However, the model consistently underestimates wind variability and extreme wind events, especially at coastal stations, where correlation coefficients dropped to 0.35. Wind direction analysis highlighted ERA5’s ability to replicate dominant northwest wind patterns. However, it reveals notable biases and underrepresented variability during transitional seasons. Taylor diagrams and error metrics further emphasize ERA5’s challenges in capturing localized dynamics influenced by land-sea interactions. Enhancements such as localized calibration using high-resolution datasets, hybrid models incorporating machine learning techniques, and long-term monitoring networks are recommended to improve accuracy. By addressing these limitations, ERA5 can more effectively support engineering applications, including coastal infrastructure design and renewable energy development, while advancing Kuwait’s sustainable development goals. This study provides valuable insights into refining reanalysis model performance in complex coastal environments. Full article

With the evolvement of the coal marketplace and massive growth in renewable resource power, conventional coal-fired generation is facing challenges in the operation of fluctuating loads and varying coal characteristics. The intent of this study is to predict carbon emissions from coal-fired power plants during load cycling and the operation of varying coal characteristics. The given correlation was revised by adding a new nitrogen term and using thermodynamic data from the latest JANAF tables. On the basis of the revised correlation, the quantitative impact of each element composition of coal on the carbon emission factor was worked out according to first-order Taylor series approximation. The O/C and H/C ratio of coal at the lowest carbon emission factor was evaluated in the VAN Krevelen diagram, showing that coals have the lowest carbon emission factor value of roughly 23.25 kg/GJ GCV at atomic O/C and H/C ratio values of about 0.08 and 0.98, respectively. Correlations of carbon emission with the proximate analysis of coal were established through stepwise linear regression using 247 coals for power generation. Based on the varying nature of the net heat rate with load condition expressed by the generic model derived from 11 typical units in-service, the impact of coal and load cycling on carbon emission was captured with a developed equation. Linking the above investigation to a study in a thermal power unit with a rated output of 1000 MW shows that the total variation of carbon emission due to the combined effect of coal and load cycling could be 27.44% if the unit cycles at 35% to 100% rated output with coal normally varying in the present context. Full article

This study evaluated the effectiveness of the NASA Earth Exchange Global Daily Downscaled models from CMIP6 experiments (hereafter; NEX-GDDP-CMIP6) in reproducing observed precipitation and temperature across East Africa (EA) from 1981 to 2014. Additionally, climate changes were estimated under various emission scenarios, namely low (SSP1-2.6), medium (SSP2-4.5), and high (SSP5-8.5) scenarios. Multiple robust statistics metrics, the Taylor diagram, and interannual variability skill (IVS) were employed to identify the best-performing models. Significant trends in future precipitation and temperature are evaluated using the Mann-Kendall and Sen’s slope estimator tests. The results highlighted IPSL-CM6A-LR, EC-Earth3, CanESM5, and INM-CM4-8 as the best-performing models for annual and March to May (MAM) precipitation and temperature respectively. By the end of this century, MAM precipitation and temperature are projected to increase by 40% and 4.5 °C, respectively, under SSP5-8.5. Conversely, a decrease in MAM precipitation and temperature of 5% and 0.8 °C was projected under SSP2-4.5 and SSP1-2.6, respectively. Long-term mean precipitation increased in all climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with near-term MAM precipitation showing a 5% decrease in Rwanda, Burundi, Uganda, and some parts of Tanzania. Under the SSP5-8.5 scenario, temperature rise exceeded 2–6 °C in most regions across the area, with the fastest warming trend of over 6 °C observed in diverse areas. Thus, high greenhouse gas (GHG) emission scenarios can be very harmful to EA and further GHG control is needed. Full article

Accurate estimation of construction costs is of foremost importance in construction management processes. Considering the changes and unexpected situations, cost estimations should be revised during the construction process. This study investigates the predictability of earned value management (EVM)-based approaches using machine learning (ML) methods. A total of 2318 data points via 19 EVM-based cost estimation methods were created and six ML methods were used for the analyses. The planned and actual project data of the rough construction activities of a housing project completed in Türkiye were used. The ML methods considered consisted of adaptive neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), Gaussian process regression (GPR), long-short-term memory (LSTM), M5 model trees (M5TREEs), and support vector machines (SVMs). The created models were compared using performance criteria such as mean absolute percentage error (MAPE), relative root means square error (RRMSE), coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and overall index of model performance (OI). Moreover, radar charts, trend graphs, Taylor diagrams, violin plots, and error boxplots were used to evaluate the performance of the estimation models. The results revealed that the classical ANN model outperforms EVM-based cost methods that utilize current ML methods. Full article

The most significant factor affecting the performance of a solar photovoltaic (PV) system is its tilt angle. It determines the amount of incident solar energy at the panel surface. In this paper, the optimum tilt angle of solar PV panels is estimated based on measured data recorded in twelve major cities in Libya by changing the panel’s tilt angle from $0^{\circ }$ up to ${90}^{\circ }$ in steps of $1^{\circ }$ and searching for the corresponding maximum daily total solar radiation. A non-linear regression technique was applied to establish six empirical models to determine the optimum tilt angle in Libya. The accuracy of the models was evaluated using statistical criteria such as Taylor diagrams, root mean square error, mean bias error, and correlation coefficient. The results demonstrated that the monthly optimum tilt angle increased during the winter and decreased during the summer varying from $0^{\circ }$ to ${59}^{\circ }$. In addition, both third-order polynomial and Fourier models presented the best efficiency in estimating the optimum tilt angle with a correlation coefficient of 0.9943. The percent gain in average yearly solar energy received at the monthly optimum tilt angle varies from 12.43% to 17.24% for all studied sites compared to the horizontal surface. Full article

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models. Full article