The purpose of this paper is to propose a more detailed classification method for the orientation of machiya buildings based on the survey results using the Internet map function. The results of this paper reveal: 1) ...
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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 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.
Understanding the strength behavior and leaching characteristics of mining tailings stabilized with alkali-activated cements in the short, medium, and long term is crucial for the feasibility of material applications....
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Herein, a low-cost and readily available sodium aluminate (NaAlO2) was used as a solid base catalyst for the depolymerization of polycarbonate (PC) via methanolysis in the presence of tetrahydrofuran (THF) as a solven...
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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.
This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 11...
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This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 115 samples from different sources,taking into account nine key SCC *** validation of the proposed model was evaluated via six indices,including correlation coefficient(R),mean squared error,mean absolute error(MAE),IA,Slope,and mean absolute percentage *** addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence *** results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s *** that,an examination of the parameters impacting the CS of SCC was provided.
This paper proposes a new method for obtaining self and mutual inductances in wireless power transfer (WPT) systems using a deep neural network (DNN). In this paper, five structural parameters of a WPT system were exp...
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Magnetic coupled wireless power transfer systems have been incorporated in various devices including smartphones, and are becoming important to charge multiple devices at the same time. In this paper, we propose a new...
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With the projected global population reaching 9.7 billion by 2050, the production and consumption of food continue to escalate. Nitrogen is pivotal in this landscape due to its significance in living organisms and the...
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With the projected global population reaching 9.7 billion by 2050, the production and consumption of food continue to escalate. Nitrogen is pivotal in this landscape due to its significance in living organisms and the environmental repercussions associated with nutrient-rich waste. Inappropriate disposal of such residues contributes to nitrogen release into the environment, impacting aquatic ecosystems and precipitating the generation of greenhouse gases. This study tackles the global challenge of effectively managing nitrogen in food waste by utilizing Material Flow Analysis (MFA) as a tool to comprehend this cycle. The analysis uncovered that vegetables, legumes, and fruits constitute the primary sources of waste generation, while meats, despite their lower mass, account for a substantial proportion of total nitrogen depletion. In the surveyed month of October 2023, 174,834 meals were served, resulting in an average food consumption N-footprint of 0.003 kg of nitrogen discarded per individual meal within the restaurant’s organic waste. These findings indicate that 65% of nitrogen is consumed in meal form, while 35% is discarded as organic solid waste. However, 53% of the nitrogen in the residues originates from food preparation processes, with food preparation responsible for over half of this figure. A deeper process analysis reveals that vegetables have low nitrogen concentrations, although they significantly contribute to waste at all stages. In contrast, meats and eggs, with higher nitrogen concentrations, emerge as noteworthy contributors to the overall nitrogen content in waste. Vegetables and meats contribute about 50% and 45% of the total nitrogen, respectively. These outcomes substantially enhance our comprehension of waste generation dynamics and nutrient utilization at the university restaurant, assisting with waste management, design of sustainable menus, reduction of food waste, and optimized resource utilization, contributing to sustainability and re
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