Spurious forces are a significant challenge for multi-scale methods,e.g.,the coupled atomistic/discrete dislocation(CADD)method.The assumption of isotropic matter in the continuum domain is a critical factor leading t...Spurious forces are a significant challenge for multi-scale methods,e.g.,the coupled atomistic/discrete dislocation(CADD)method.The assumption of isotropic matter in the continuum domain is a critical factor leading to such forces.This study aims to minimize spurious forces,ensuring that atomic dislocations experience more precise forces from the continuum domain.The authors have already implemented this idea using a simplified and unrealistic slipping system.To create a comprehensive and realistic model,this paper considers all possible slip systems in the face center cubic(FCC)lattice structure,and derives the required relationships for the displacement fields.An anisotropic version of the three-dimensional CADD(CADD3D)method is presented,which generates the anisotropic displacement fields for the partial dislocations in all the twelve slip systems of the FCC lattice structure.These displacement fields are tested for the most probable slip systems of aluminum,nickel,and copper with different anisotropic levels.Implementing these anisotropic displacement fields significantly reduces the spurious forces on the slip systems of FCC materials.This improvement is particularly pronounced at greater distances from the interface and in more anisotropic materials.Furthermore,the anisotropic CADD3D method enhances the spurious stress difference between the slip systems,particularly for materials with higher anisotropy.展开更多
Bridge networks are essential components of civil infrastructure,supporting communities by delivering vital services and facilitating economic activities.However,bridges are vulnerable to natural disasters,particularl...Bridge networks are essential components of civil infrastructure,supporting communities by delivering vital services and facilitating economic activities.However,bridges are vulnerable to natural disasters,particularly earthquakes.To develop an effective disaster management strategy,it is critical to identify reliable,robust,and efficient indicators.In this regard,Life-Cycle Cost(LCC)and Resilience(R)serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans.This study proposes an innova-tive LCC-R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan.The proposed framework employs both single-and multi-objective opti-mization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks.The considered retrofit strategies include various options such as different mate-rials(steel,CFRP,and GFRP),thicknesses,arrangements,and timing of retrofitting actions.The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incre-mental dynamic analyses for each case.In the subsequent step,the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function.Next,the LCC is evaluated according to the pro-posed formulation for multiple seismic occurrences,which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events.For optimization purposes,the Non-Dominated Sorting Genetic Algorithm II(NSGA-II)evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions.The study presents the most effective retrofit strategies for an illustrative bridge network,providing a comprehensive discussion and insights into the resulting tactical approaches.The findings underscore that the methodologies employed lead to logical and actionable retrofit strategies,paving the way for enhanced resilience and cost-effectiveness in bridge network management against seismic hazards.展开更多
In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear s...In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.展开更多
The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel.Therefore,the chloride concentration in concrete is a vital parameter for estimatin...The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel.Therefore,the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel.This 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 measurement.The 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 content.When 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 processes.This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.展开更多
The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft soi.In general,the C_(v) is determined experimentally in the laboratory.However,the experimental test...The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft soi.In general,the C_(v) is determined experimentally in the laboratory.However,the experimental tests are time-consuming as well as expensive.Therefore,researchers tried several ways to determine C_(v) via other simple soil parameters.In this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of soil.To conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for modeling.The 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 inputs.The 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.展开更多
Nanofiltration(NF)using loose membranes has a high application potential for advanced treatment of drinking water by selectively removing contaminants from the water,while membrane fouling remains one of the biggest p...Nanofiltration(NF)using loose membranes has a high application potential for advanced treatment of drinking water by selectively removing contaminants from the water,while membrane fouling remains one of the biggest problems of the process.This paper reported a seven-month pilot study of using a loose NF membrane to treat a sand filtration effluent which had a relatively high turbidity(∼0.4 NTU)and high concentrations of organic matter(up to 5 mg/L as TOC),hardness and sulfate.Results showed that the membrane demonstrated a high rejection of TOC(by<90%)and a moderately high rejection of two pesticides(54%–82%)while a moderate rejection of both calcium and magnesium(∼45%)and a low rejection of total dissolved solids(∼27%).The membrane elements suffered from severe membrane fouling,with the membrane permeance decreased by 70%after 85 days operation.The membrane fouling was dominated by organic fouling,while biological fouling was moderate.Inorganic fouling was mainly caused by deposition of aluminum-bearing substances.Though inorganic foulants were minor contents on membrane,their contribution to overall membrane fouling was substantial.Membrane fouling was not uniform on membrane.While contents of organic and inorganic foulants were the highest at the inlet and outlet region,respectively,the severity of membrane fouling increased from the inlet to the outlet region of membrane element with a difference higher than 30%.While alkaline cleaning was not effective in removing the membrane foulants,the use of ethylenediamine tetraacetate(EDTA)at alkaline conditions could effectively restore the membrane permeance.展开更多
Swimming has become a popular exercising and recreational activity in China but little is known about the disinfection by-products （DBPs） concentration levels in the pools. This study was conducted as a survey of th...Swimming has become a popular exercising and recreational activity in China but little is known about the disinfection by-products （DBPs） concentration levels in the pools. This study was conducted as a survey of the DBPs in China swimming pools, and to establish the correlations between the DBP concentrations and the pool water quality parameters. A total of 14 public indoor and outdoor pools in Beijing were included in the survey. Results showed that the median concentrations for total tfihalomethanes （TTHM）, nine haloacetic acids （HAA9）, chloral hydrate （CH）, four haloacetonitriles （HAN4）, 1,1- dichloropropanone, 1,1,1-trichloropropanone and trichlor- onitromethane were 33.8, 109.1, 30.1, 3.2, 0.3, 0.6 pg＇L-1 and below detection limit, respectively. The TTHM and HAA9 levels were in the same magnitude of that in many regions of the world. The levels of CH and nitrogenous DBPs were greatly higher than and were comparable to that in typical drinking water, respectively. Disinfection by chlorine dioxide or trichloroisocyanuric acid could sub- stantially lower the DBP levels. The outdoor pools had higher TTHM and HAA9 levels, but lower trihaloacetic acids （THAA） levels than the indoor pools. The TTHM and HAA9 concentrations could be moderately correlated with the free chlorine and total chlorine residuals but not with the total organic carbon （TOC） contents. When the DBP concentration levels from other survey studies were also included for statistical analysis, a good correlation could be established between the TTHM levels and the TOC concentration. The influence of chlorine residual on DBP levels could also be significant.展开更多
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 determined.In the machine learning(ML)approa...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 determined.In 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 models.This study developed different ML models for predicting the CS of FRSCC to address these limitations.Artificial 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 development.The 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 FRSCC.The results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.展开更多
Climate change will profoundly affect hydrological processes at various temporal and spatial scales.This study is focused on assessing the alteration of water resources availability and low flows frequencies driven by...Climate change will profoundly affect hydrological processes at various temporal and spatial scales.This study is focused on assessing the alteration of water resources availability and low flows frequencies driven by changing climates in different time periods of the 21st century.This study evaluates the adaptability of prevailing Global Circulation Models(GCMs)on a particular watershed through streamflow regimes.This analysis was conducted in the Great Miami River Watershed,Ohio by analyzing historical and future simulated streamflow using 10 climate model outputs and the Soil and Water Assessment Tool(SWAT).The climate change scenarios,consisting of ten downscaled Coupled Model Intercomparision Project Phase 5(CMIP5)climate models in combination with two Representative Concentration Pathways(RCP 4.5 and RCP 8.5)were selected based on the correlation between observed records and model outputs.Streamflow for three future periods,2016-2043,2044-2071 and 2072-2099,were independently analyzed and compared with the baseline period(1988-2015).Results from the average of ten models projected that 7-day low flows in the watershed would increase by 19%in the 21st century under both RCPs.This trend was also consistent for both hydrological(7Q10,1Q10)and biological low flow statistics(4B3,1B3).Similarly,average annual flow and monthly flows would also increase in future periods,especially in the summer.The flows simulated by SWAT in response to the majority of climate model projections showed a consistent increase in low flow patterns.However,the flow estimates using the Max-Planck-Institute Earth System Model(MPI-ESM-LR)climate output resulted in the biological based low flows(4B3,1B3)decreasing by 22.5%and 33.4%under RCP 4.5 and 56.9%and 63.7%under RCP 8.5,respectively,in the future when compared to the baseline period.Regardless,the low flow ensemble from the 10 climate models for the 21st century seemed to be slightly higher than that of historical low flows.展开更多
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...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 factors.The 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 error.In addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots.The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS.Following that,an examination of the parameters impacting the CS of SCC was provided.展开更多
文摘Spurious forces are a significant challenge for multi-scale methods,e.g.,the coupled atomistic/discrete dislocation(CADD)method.The assumption of isotropic matter in the continuum domain is a critical factor leading to such forces.This study aims to minimize spurious forces,ensuring that atomic dislocations experience more precise forces from the continuum domain.The authors have already implemented this idea using a simplified and unrealistic slipping system.To create a comprehensive and realistic model,this paper considers all possible slip systems in the face center cubic(FCC)lattice structure,and derives the required relationships for the displacement fields.An anisotropic version of the three-dimensional CADD(CADD3D)method is presented,which generates the anisotropic displacement fields for the partial dislocations in all the twelve slip systems of the FCC lattice structure.These displacement fields are tested for the most probable slip systems of aluminum,nickel,and copper with different anisotropic levels.Implementing these anisotropic displacement fields significantly reduces the spurious forces on the slip systems of FCC materials.This improvement is particularly pronounced at greater distances from the interface and in more anisotropic materials.Furthermore,the anisotropic CADD3D method enhances the spurious stress difference between the slip systems,particularly for materials with higher anisotropy.
文摘Bridge networks are essential components of civil infrastructure,supporting communities by delivering vital services and facilitating economic activities.However,bridges are vulnerable to natural disasters,particularly earthquakes.To develop an effective disaster management strategy,it is critical to identify reliable,robust,and efficient indicators.In this regard,Life-Cycle Cost(LCC)and Resilience(R)serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans.This study proposes an innova-tive LCC-R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan.The proposed framework employs both single-and multi-objective opti-mization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks.The considered retrofit strategies include various options such as different mate-rials(steel,CFRP,and GFRP),thicknesses,arrangements,and timing of retrofitting actions.The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incre-mental dynamic analyses for each case.In the subsequent step,the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function.Next,the LCC is evaluated according to the pro-posed formulation for multiple seismic occurrences,which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events.For optimization purposes,the Non-Dominated Sorting Genetic Algorithm II(NSGA-II)evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions.The study presents the most effective retrofit strategies for an illustrative bridge network,providing a comprehensive discussion and insights into the resulting tactical approaches.The findings underscore that the methodologies employed lead to logical and actionable retrofit strategies,paving the way for enhanced resilience and cost-effectiveness in bridge network management against seismic hazards.
基金supported by Prince Sultan University(Grant No.PSU-CE-TECH-135,2023).
文摘In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.
文摘The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel.Therefore,the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel.This 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 measurement.The 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 content.When 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 processes.This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.
文摘The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft soi.In general,the C_(v) is determined experimentally in the laboratory.However,the experimental tests are time-consuming as well as expensive.Therefore,researchers tried several ways to determine C_(v) via other simple soil parameters.In this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of soil.To conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for modeling.The 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 inputs.The 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.
文摘Nanofiltration(NF)using loose membranes has a high application potential for advanced treatment of drinking water by selectively removing contaminants from the water,while membrane fouling remains one of the biggest problems of the process.This paper reported a seven-month pilot study of using a loose NF membrane to treat a sand filtration effluent which had a relatively high turbidity(∼0.4 NTU)and high concentrations of organic matter(up to 5 mg/L as TOC),hardness and sulfate.Results showed that the membrane demonstrated a high rejection of TOC(by<90%)and a moderately high rejection of two pesticides(54%–82%)while a moderate rejection of both calcium and magnesium(∼45%)and a low rejection of total dissolved solids(∼27%).The membrane elements suffered from severe membrane fouling,with the membrane permeance decreased by 70%after 85 days operation.The membrane fouling was dominated by organic fouling,while biological fouling was moderate.Inorganic fouling was mainly caused by deposition of aluminum-bearing substances.Though inorganic foulants were minor contents on membrane,their contribution to overall membrane fouling was substantial.Membrane fouling was not uniform on membrane.While contents of organic and inorganic foulants were the highest at the inlet and outlet region,respectively,the severity of membrane fouling increased from the inlet to the outlet region of membrane element with a difference higher than 30%.While alkaline cleaning was not effective in removing the membrane foulants,the use of ethylenediamine tetraacetate(EDTA)at alkaline conditions could effectively restore the membrane permeance.
文摘Swimming has become a popular exercising and recreational activity in China but little is known about the disinfection by-products （DBPs） concentration levels in the pools. This study was conducted as a survey of the DBPs in China swimming pools, and to establish the correlations between the DBP concentrations and the pool water quality parameters. A total of 14 public indoor and outdoor pools in Beijing were included in the survey. Results showed that the median concentrations for total tfihalomethanes （TTHM）, nine haloacetic acids （HAA9）, chloral hydrate （CH）, four haloacetonitriles （HAN4）, 1,1- dichloropropanone, 1,1,1-trichloropropanone and trichlor- onitromethane were 33.8, 109.1, 30.1, 3.2, 0.3, 0.6 pg＇L-1 and below detection limit, respectively. The TTHM and HAA9 levels were in the same magnitude of that in many regions of the world. The levels of CH and nitrogenous DBPs were greatly higher than and were comparable to that in typical drinking water, respectively. Disinfection by chlorine dioxide or trichloroisocyanuric acid could sub- stantially lower the DBP levels. The outdoor pools had higher TTHM and HAA9 levels, but lower trihaloacetic acids （THAA） levels than the indoor pools. The TTHM and HAA9 concentrations could be moderately correlated with the free chlorine and total chlorine residuals but not with the total organic carbon （TOC） contents. When the DBP concentration levels from other survey studies were also included for statistical analysis, a good correlation could be established between the TTHM levels and the TOC concentration. The influence of chlorine residual on DBP levels could also be significant.
基金the University of Transport Technology,Thanh Xuan,Hanoi,Vietnam(UTT)(No.DTTD2022-07).
文摘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 determined.In 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 models.This study developed different ML models for predicting the CS of FRSCC to address these limitations.Artificial 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 development.The 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 FRSCC.The results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.
文摘Climate change will profoundly affect hydrological processes at various temporal and spatial scales.This study is focused on assessing the alteration of water resources availability and low flows frequencies driven by changing climates in different time periods of the 21st century.This study evaluates the adaptability of prevailing Global Circulation Models(GCMs)on a particular watershed through streamflow regimes.This analysis was conducted in the Great Miami River Watershed,Ohio by analyzing historical and future simulated streamflow using 10 climate model outputs and the Soil and Water Assessment Tool(SWAT).The climate change scenarios,consisting of ten downscaled Coupled Model Intercomparision Project Phase 5(CMIP5)climate models in combination with two Representative Concentration Pathways(RCP 4.5 and RCP 8.5)were selected based on the correlation between observed records and model outputs.Streamflow for three future periods,2016-2043,2044-2071 and 2072-2099,were independently analyzed and compared with the baseline period(1988-2015).Results from the average of ten models projected that 7-day low flows in the watershed would increase by 19%in the 21st century under both RCPs.This trend was also consistent for both hydrological(7Q10,1Q10)and biological low flow statistics(4B3,1B3).Similarly,average annual flow and monthly flows would also increase in future periods,especially in the summer.The flows simulated by SWAT in response to the majority of climate model projections showed a consistent increase in low flow patterns.However,the flow estimates using the Max-Planck-Institute Earth System Model(MPI-ESM-LR)climate output resulted in the biological based low flows(4B3,1B3)decreasing by 22.5%and 33.4%under RCP 4.5 and 56.9%and 63.7%under RCP 8.5,respectively,in the future when compared to the baseline period.Regardless,the low flow ensemble from the 10 climate models for the 21st century seemed to be slightly higher than that of historical low flows.
文摘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 factors.The 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 error.In addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots.The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS.Following that,an examination of the parameters impacting the CS of SCC was provided.
