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.
The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities,leading to severe flooding and significant economic losses for *** trend highlights the urgent ...
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The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities,leading to severe flooding and significant economic losses for *** trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians,which is often overlooked in traditional flood hazard *** study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods,with a specific focus on Cúcuta,*** methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological *** computational algorithms for image recognition were used to measure water levels at 5-s intervals on November 6,2020,using drones for digital elevation model data *** Cúcuta,where flood risk is high and drainage infrastructure is limited,the PCSWMM(Computer-based Urban Stormwater Management Model)was calibrated and validated to simulate extreme flood *** model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban *** return periods(5,10,50,100),with extreme rainfall of 3 h,were used to estimate the variability of the risk *** output of the model was analyzed,and an integrated and time-varying comparison of the results was *** show that the regions of high-water depth and high velocity could vary significantly along the duration of the different extreme ***,from 5 to 100 years return period,the percentage of area at risk increased from 9.6%to 16.6%.The pedestrian sensitivity appears much higher than the increase in velocities or water depth *** study identified medium to high-risk locations,which are dynamic in *** can conclude dynamics are spatiotemporal,and the added information layer of pedestrians brings vulnerability information that is a
This study presents an analytical methodology for predicting the natural frequencies of hollow-core slab panels, addressing a significant research and application need in structural engineering. The study suggests a n...
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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.
The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft *** general,the C_(v) is determined experimentally in the ***,the experimental tests are time-consumi...
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The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft *** general,the C_(v) is determined experimentally in the ***,the experimental tests are time-consuming as well as ***,researchers tried several ways to determine C_(v) via other simple soil *** this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of *** conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for *** performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 *** results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters.
Emerging contaminants(ECs)in drinking water pose threats to public health due to their environmental prevalence and potential *** occurrence of ECs in our drinking water supplies depends on their physicochemical prope...
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Emerging contaminants(ECs)in drinking water pose threats to public health due to their environmental prevalence and potential *** occurrence of ECs in our drinking water supplies depends on their physicochemical properties,discharging rate,and susceptibility to removal by water treatment *** health effects of long-term exposure to ECs justify their regular monitoring in drinking water *** this review article,we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy(SERS)for EC analysis in drinking *** principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost,time,sensitivity,and availability is ***,we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per-and polyfluoroalkyl substances,novel pesticides,pharmaceuticals,endocrine-disrupting chemicals,and *** addition to maximizing the intrinsic enhancement factors of SERS substrates,strategies to improve hot spot accessibilities to the targeting ECs are equally *** is a review article focusing on SERS analysis of ECs in drinking *** discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.
The sustainable built environment concept has recently gained enormous attention from academic and industrial organizations. The growth in climate-related disasters and pandemics, continuing difficulties in the energy...
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In recent decades, frequently reported heat waves attributed to climate change have profoundly affected human health and food production and posed challenges to the sustainable development goals of a densely populated...
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Soil's engineering properties are critical components in designing sub and super structures. Knowing the engineering properties helps engineers determine the appropriate type of foundation for a building. Determin...
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