The Effect of Microwave Curing on the Strength Development of Class-F Fly Ash-Based Geopolymer Mortar

This study investigates the influence of microwave (MW) curing on thestrength development of geopolymer. Since applying conventional oven heat curingmakes the heat move from the outer edge to the center of the specimen, it leads to anon-uniform of distributing heat within the specimen, which affects the mechanicalproperties of the geopolymer. On the other hand, the use of MW reduces the curingtime and allows uniform heat distribution within samples, and provides highermechanical properties in a short period. The influence of conventional heat curingand MW curing on class F fly ash based geopolymer activated with sodiumhydroxide and sodium metasilicate was investigated. The conventional heat curingwas applied at 75 and 90°C for 6 and 24 hours; on the other hand, additional MWcuring was applied for a different period (5-60 minutes) and different energy level(100, 180 and 300W) on hardened geopolymer samples cured with conventionaloven curing. The results show that the use of conventional heat curing for 6 hours,followed by MW curing, gave higher or equivalent strength compared to onlyconventional heat curing. While 24 hours conventional heat curing results with ageopolymer having 39.1 MPa compressive strength, 6 hours conventional heatcuring followed by 1 hour MW curing at 180W energy level results with ageopolymer with compressive strength in the order of 80 MPa.&nbsp;</p

Study of Behaviour of Short Concrete Columns Confined with PVC Tube under Uniaxial Load

An experimental investigation has been carried out to evaluate the effectiveness of Polyvinyl chloride (PVC) confinements in short plain circular concrete columns. The experimental part is conducted using different PVC tube diameters (110, 160, 220, and 250 mm) with two types of confinement strategies (fully and confined with the cut ends). The results are validated with unconfined samples (control samples). The test results showed that using external confinement of concrete columns by PVC tubes enhances the ultimate load capacity of the short columns. For fully confined samples, the enhancement ratio ranges between 5% and 8.3%, and from 4.16% to 15% for samples with cut ends. Furthermore, the confining of PVC pipes with the cut ends (CCC) has a more considerable effect on load capacity for all diameters except the ones with 250 mm, where the samples with full confinement (Cc) carried a bigger load than those with cutting ends. Finally, a numerical simulation of samples is carried out by the finite element (FE) model using the ABAQUS software. For all scenarios, the results of the numerical analysis showed considerable similarity to the experimental results, with R2 of 0.95 indicating the high linearity between the actual and simulated compressive strength values. Moreover, the FE induces fewer simulated errors with a relative error (RE) ranging from 0.16% to 6% for all scenarios

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.Validerad;2022;Nivå 2;2022-10-03 (hanlid);Funder: Al-Maarif University College, Ramadi, Iraq</p

Introducing high-order response surface method for improving scour depth prediction downstream of weirs

Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.Full text license: CC BY 4.0; </p

Data-driven models for atmospheric air temperature forecasting at a continental climate region

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.Validerad;2022;Nivå 2;2022-11-07 (joosat);</p

Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques

Publisher Copyright: © 2023 by the authors.Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR–SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR–SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR–SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.Peer reviewe