Recycled aggregate concrete (RAC) is commonly used to lessen the environmental effect of concrete building and demolition waste. The compressive strength of the RAC is one of the most critical factors influencing concrete quality. The compressive strength is assessed by a compression test, which takes a large number of materials and is expensive and time-consuming. With the development of novel concrete mixes and applications, academics are obliged to seek accurate models for forecasting mechanical strength. A significant source of difficulty in compressive strength modeling is that there are many mixture components and testing conditions whose variation significantly influences the predicted values. To this end, this study explores the mixture design of sustainable concrete in order to generate eco-friendly concrete mixes. Tests are conducted on 18 different mixtures comprising different proportions of waste tires, plastic, cement, and red brick to experiment with new green RAC mixtures. For the modeling part, the deep residual neural networks (DRNNs) method is first presented to the problem, aided by a database from the literature for a pretraining task. The proposed DRNNs structure uses shortcuts (i.e., residual connections) that bypass some layers in the deep network structure to alleviate the problem of training with high accuracy. The performance of the proposed DRNNs is evaluated using different goodness of fit measures and compared with well-known machine learning tools. The findings showed that the suggested model could provide credible predictions about the desired mechanical parameter, saving the required lab efforts by 40 %. Finally, a variance-based global sensitivity analysis is performed with the Latin hypercube simulation method to help rank/prioritize each mixture component's impact on determining the compressive strength in practice while mitigating the potential misrepresentation of results due to the correlations between the input parameters. The analysis showed that cement and waste contents are the most significant ones in their first and total order effects.

Pumping water for irrigation systems using renewable energy is one of the broad and viable uses. The use of renewable energy for pumping water is a perfect solution in places far from the electrical grids because the cost of connecting an electric pump to the utility network becomes expensive and sometimes difficult. Photovoltaic systems are the most common and abundant type of renewable energy used to overcome this challenge. Therefore, this paper presents a methodology for improvement the size of the PV system and calculating all the elements of this system from the PV water pumping system, water quantity needed for irrigation, the sort of plant, the microclimate, the soil, and the irrigation method to obtain the optimum electric power needed to pump the water. In the proposed work, the Farafra Oasis was chosen as a case study, an Egyptian oasis located in the Western Desert within the borders of the New …

¹ The potential to integrate multicarrier energy systems that fulfill the rapidly increasing energy demand is being made possible by combining renewable energy sources (RES) encompassing wind and solar into massive-scale fossil fuel power generating stations. This article provides a thorough overview of the energy hub while highlighting its benefits, such as optimizing energy consumption and reducing greenhouse gas emissions. Energy hub systems are considered the future trendsetter for energy systems. A number of their merits over conventional energy systems are reported in this article. Solar farms, wind turbines, boilers, power-to-gas (P2G) units, fossil-fueled combined cycle power plants (CCPPs), and electric and thermal storing units are components of an energy hub that generates and transforms energy. Levelized cost of energy (LCOE), Levelized C02 emission (LC02), and Levelized Cost of …

Climate change and limited power supplies receive significant incentives to develop alternative paving materials and technologies. Reclaimed asphalt pavement (RAP) and Warm Mix Asphalt (WMA) are technologies that can provide significant benefits to both the environment and the economy. This study used response surface methodology (RSM) to analyze the rutting resistance and moisture susceptibility of asphalt mixtures containing different amounts of RAP and doses and types of WMA. The experimental design was established utilizing the RSM with a central composite design (CCD) for varying RAP dosages (25–50%), WMA amounts (1.5-4%), and WMA types (waxy organic Asphaltan type A ® , and waxy organic Asphaltan type B ® ). The moisture sensitivity and rutting resistance of asphalt samples were evaluated using the Modified Lottman method (AASHTO T 283) and wheel tracking test, respectively. RSM’s statistical and mathematical models were employed to estimate the optimal value for RAP dose and WMA content and type. The results demonstrated that adding RAP to WMA mixtures increased the rutting and moisture resistance of asphalt samples. Also, the analysis of variance (ANOVA) results indicated that the increase in the WMA content led to a significant decrease in the rutting resistance, while the rise in the RAP content contributed to a significant enhancement in the rutting performance of the samples. The statistical outcome also showed that the moisture susceptibility of the mixture decreased significantly after increasing the RAP content, while the increase in WMA content did not have a significant influence on the moisture performance of the mix regardless of the WMA type. The research indicated that rutting resistance and moisture susceptibility have significant correlation coefficients (R2) of > 0.92, indicating that the model is highly correlated with the experimental results. Multi-objective numerical optimization led to the optimal design with 50% RAP and 1.5% WMA-type A. Validation findings indicate strong agreement and model effectiveness, with an error variance of less than 5% for all responses.

Controlling the hydraulic heads along a coastal aquifer may help to effectively manage
saltwater intrusion, improve the conventional barrier’s countermeasure, and ensure the coastal
aquifer’s long-term viability. This study proposed a framework that utilizes a decision-making model
(DMM) by incorporating the results of two other models (physical and numerical) to determine proper
countermeasure components. The physical model is developed to analyze the behavior of saltwater
intrusion in unconfined coastal aquifers by conducting two experiments: one for the base case, and one
for the traditional vertical barrier. MODFLOW is used to create a numerical model for the same aquifer,
and experimental data are used to calibrate and validate it. Three countermeasure combinations,
including vertical barrier, surface, and subsurface recharges, are numerically investigated using
three model case categories. Category (a) model cases investigate the hydraulic head’s variation
along the aquifer to determine the best recharge location. Under categories (b) and (c), the effects
of surface and subsurface recharges are studied separately or in conjunction with a vertical barrier.
As a pre-set of the DMM, evaluation and classification ratios are created from the physical and
numerical models, respectively. The evaluation ratios are used to characterize the model case results,
while the classification ratios are used to classify each model case as best or worst. An analytical
hierarchy process (AHP) as a DMM is built using the hydraulic head, salt line, repulsion, wedge area,
and recharge as selection criteria to select the overall best model case. According to the results, the
optimum recharging location is in the length ratio (LR) from 0.45 to 0.55. Furthermore, the DMM
supports case3b (vertical barrier + surface recharge) as the best model case to use, with a support
percentage of 48%, implying that this case has a good numerical model classification with a maximum
repulsion ratio (Rr) of 29.4%, and an acceptable wedge area ratio (WAR) of 1.25. The proposed
framework could be used in various case studies under different conditions to assist decision-makers
in evaluating and controlling saltwater intrusion in coastal aquifers.