The construction of Social Interest Housing through housing programs is a crucial government investment aimed at reducing housing deficits and ensuring housing rights for low-income populations. However, despite drivi...
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The exponential increase in the construction of energy generation systems using wind turbines has driven the need for taller towers in search of stronger winds, resulting in challenges for the development of structura...
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Increasing threats to global water resources from climate change emphasize the importance of comprehending its impacts on hydrology for effective water management. This article quantifies climate input uncertainty in ...
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The principles of circular economy encourage the recovery of phosphorus from nutrient-rich waste streams such as animal manure, domestic wastewater, and urine to supplement existing sources of raw phosphorus. However,...
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In Central Vietnam, the aggregate typically originates from highly acidic rocks containing a significant amount of silicon dioxide (SiO2), leading to poor adhesion quality with virgin asphalt binder. Hence, the asphal...
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Operational strategies have been applied in constructed wetlands to optimize the removal of nutrients and hormones that are still a concern in wastewater treatment. The strategy of intensifying intermittent aeration w...
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Operational strategies have been applied in constructed wetlands to optimize the removal of nutrients and hormones that are still a concern in wastewater treatment. The strategy of intensifying intermittent aeration was investigated in two microcosm-scale vertical-flow constructed wetlands (VFCWs) planted with Eichhornia crassipes onto autoclaved aerated concrete (AC) in the removal of nutrients, estrone (E1), 17β-estradiol (E2) and 17α-ethinylestradiol (EE2). CW-1 (2.4 LO2 min−1) and CW-2 (1.4 LO2 min−1) were fed with synthetic wastewater in sequencing-batch mode (cycles 48-48-72 h) and intermittently aerated for 1 h, followed by 7 h without aeration for 377 days. Combined with the intensification strategy, the use of planted free-floating macrophytes and concrete-based material (emergent) as filtering media stand out as the innovation and originality aspects of this study. Despite the hormone addition, intensifying aeration enhanced the efficiencies since CW-1 achieved the highest removals with 91% COD, 77% TN, 74% TAN, 60% nitrate, and 97% TP in Stage I (no hormone addition) and 90% COD, 80% TN, 93% TAN, 63% nitrate, and 82% TP in Stage II (with hormone addition). CW-1 achieved the highest removal efficiencies of E1 (84%), E2 (95%), and EE2 (73%). Conversely, the efficiencies decreased under the lower aeration rate (in CW-2) for all parameters. Macrophyte uptake and adsorption stood out for TN (>60.25%) and TP (>27.6%) removal as the main mechanisms in the VFCWs. The characteristics of AC favored ion exchange and precipitation, reinforcing the potential of this material as filtering media in VFCWs. Intensification of intermittent aeration combined with hormone addition diverse and riched the microbial community with the presence of Thauera, Lentimicrobium (denitrification), Candidatus Accumulibacter (phosphorus removal), Pseudomonas, Fusibacter, and Azoarcus (EE2 degradation). Intensifying intermittent aeration was an important strategy to enhance the simultaneou
Contamination of aquatic and terrestrial organisms by Perfluoroalkyl substances (PFAS), emerging contaminants, is widespread, as these compounds are present in water, soil, air, and food, owing to their environmental ...
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Ceramic membranes have emerged as a promising solution for microfiltration processes, offering advantages such as cost-effectiveness, environmental benefits, and the utilization of abundant raw materials derived from ...
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The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement ***,the chloride concentration in concrete is a vital parameter for estimating the corros...
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The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement ***,the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement *** research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting(GB),artificial neural network(ANN),and random forest(RF)in combination with particle swarm optimization(PSO).The input variables for modeling include exposure condition,water/binder ratio(W/B),cement content,silica fume,time exposure,and depth of *** results indicate that three models performed well with high accuracy of prediction(R2⩾0.90).Among three hybrid models,the model using GB_PSO achieved the highest prediction accuracy(R2=0.9551,RMSE=0.0327,and MAE=0.0181).Based on the results of sensitivity analysis using SHapley Additive exPlanation(SHAP)and partial dependence plots 1D(PDP-1D),it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride *** the number of different exposure conditions is larger than two,the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical *** study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.
Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately *** the machine learning(ML)approach to esti...
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Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately *** the machine learning(ML)approach to estimating the CS of FRSCC,the current research gaps include the limitations of samples in databases,the applicability constraints of models owing to limited mixture components,and the possibility of applying recently proposed *** study developed different ML models for predicting the CS of FRSCC to address these *** neural network,random forest,and categorical gradient boosting(CatBoost)models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique.A database of 381 samples was created,representing the most significant FRSCC dataset compared with previous studies,and it was used for model *** findings indicated that CatBoost outperformed the other two models with excellent predictive abilities(root mean square error of 2.639 MPa,mean absolute error of 1.669 MPa,and coefficient of determination of 0.986 for the test dataset).Finally,a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of *** results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.
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