To improve the prediction accuracy of the International Roughness Index(IRI)of Jointed PlainConcrete Pavements(JPCP)and Continuously Reinforced Concrete Pavements(CRCP),a machine learning approach is developed in this...To improve the prediction accuracy of the International Roughness Index(IRI)of Jointed PlainConcrete Pavements(JPCP)and Continuously Reinforced Concrete Pavements(CRCP),a machine learning approach is developed in this study for the modelling,combining an improved Beetle Antennae Search(MBAS)algorithm and Random Forest(RF)model.The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study.The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well.The results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model(RF-MBAS)in this study is higher,indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the CRCP.The accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide(MEPDG),indicating the great potential of this developed model.The importance analysis showed that the IRI of JPCP and CRCP was proportional to the corresponding input variables in this study,including the total joint faulting cumulated per KM(TFAULT),percent subgrade material passing the 0.075-mm Sieve(P_(200))and pavement surface area with flexible and rigid patching(all Severities)(PATCH)which scored higher.展开更多
Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined wi...Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.展开更多
Pervious concrete(PC)is at risk of clogging due to the continuous blockage of sand into it during its service time.This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning tec...Pervious concrete(PC)is at risk of clogging due to the continuous blockage of sand into it during its service time.This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning techniques.Based on the 84 groups of the dataset developed in the earlier study,the clogging behavior of the PC was determined by the algorithm combing the SVM(support vector machines)and particle swarm optimization(PSO)methods.The PSO algorithm was employed to adjust the hyperparameters of the SVM and verify the performance using 10-fold cross-validation.The predicting results of the developed model were assessed by the coefficient of determination(R)and root mean square error(RMSE).The importance of the influential variables on the clogging behavior of PC was evaluated as well.The results showed that the PSO algorithm can effectively adjust the hyperparameters of the SVM model and can be used to construct the predictive model for the clogging behavior of the PC.The combined algorithm has the advantage of higher reliability and validity than the random hyperparameters selection.For the verification process,the developed model was able to obtain values of 0.9469 and 1.8148 for the R and RMSE,showing that the developed machine learning model can accurately be used to evaluate and predict the clogging behavior of the PC,guiding the mix-design of PC from the perspective of durability.The size of the clogging sand is the most important parameter and the thickness of the sample is the least significant factor affecting the clogging behavior.The proportions of the smallest aggregate size and largest aggregate size are the two most important design parameters of concrete with the consideration of the relatively higher importance scores,showing these two aggregates should be given special attention in future PC design for anti-clogging purposes.展开更多
Cyanobacterial harmful algal blooms are a major threat to freshwater eco-systems globally. To deal with this threat, researches into the cyanobacteria bloom in fresh water lakes and rivers have been carried out all ov...Cyanobacterial harmful algal blooms are a major threat to freshwater eco-systems globally. To deal with this threat, researches into the cyanobacteria bloom in fresh water lakes and rivers have been carried out all over the world. This review presents an overlook of studies on cyanobacteria blooms. Conventional studies mainly focus on investigating the environmental factors influencing the blooms, with their limitation in lack of viewing the microbial community structures. Metagenomics study provides insight into the internal community structure of the cyanobacteria at the blooming, and there are researchers reported that sequence data was a better predictor than environmental factors. This further manifests the significance of the metagenomic study. However, large number of the latter appears to be confined only to present snapshoot of the microbial community diversity and structure. This type of investigation has been valuable and important, whilst an effort to integrate and coordinate the conventional approaches that largely focus on the environmental factors control, and the Metagenomics approaches that reveals the microbial community structure and diversity, implemented through machine learning techniques, for a holistic and more comprehensive insight into the cause and control of Cyanobacteria blooms, appear to be a trend and challenge of the study of this field.展开更多
The singlet and triplet excited-state refraction cross-sections of dimethyl sulfoxide (DMSO) solutions of ten zinc phthalocyanine derivatives with mono-or tetra-peripheral substituents at 532 nm were obtained by simul...The singlet and triplet excited-state refraction cross-sections of dimethyl sulfoxide (DMSO) solutions of ten zinc phthalocyanine derivatives with mono-or tetra-peripheral substituents at 532 nm were obtained by simultaneous fitting of closed-aperture Z scans with both nanosecond and picosecond pulse widths. Self-focusing of both nanosecond and picosecond laser pulses was observed in all complexes at 532-nm wavelength. The complexes with tetra-substituents at the ?-position exhibit relatively larger refraction cross-sections than the other complexes. The wavelength dependence of the singlet refraction cross-section of a representative complex was observed to be non-monotonic in the range of 470 - 550 nm.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2021QN1006)Natural Science Foundation of Hunan(Grant No.2023JJ50418)Hunan Provincial Transportation Technology Project(Grant No.202109).
文摘To improve the prediction accuracy of the International Roughness Index(IRI)of Jointed PlainConcrete Pavements(JPCP)and Continuously Reinforced Concrete Pavements(CRCP),a machine learning approach is developed in this study for the modelling,combining an improved Beetle Antennae Search(MBAS)algorithm and Random Forest(RF)model.The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study.The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well.The results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model(RF-MBAS)in this study is higher,indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the CRCP.The accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide(MEPDG),indicating the great potential of this developed model.The importance analysis showed that the IRI of JPCP and CRCP was proportional to the corresponding input variables in this study,including the total joint faulting cumulated per KM(TFAULT),percent subgrade material passing the 0.075-mm Sieve(P_(200))and pavement surface area with flexible and rigid patching(all Severities)(PATCH)which scored higher.
文摘Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.
基金the“Qihang Plan”of China University of Mining and Technology.
文摘Pervious concrete(PC)is at risk of clogging due to the continuous blockage of sand into it during its service time.This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning techniques.Based on the 84 groups of the dataset developed in the earlier study,the clogging behavior of the PC was determined by the algorithm combing the SVM(support vector machines)and particle swarm optimization(PSO)methods.The PSO algorithm was employed to adjust the hyperparameters of the SVM and verify the performance using 10-fold cross-validation.The predicting results of the developed model were assessed by the coefficient of determination(R)and root mean square error(RMSE).The importance of the influential variables on the clogging behavior of PC was evaluated as well.The results showed that the PSO algorithm can effectively adjust the hyperparameters of the SVM model and can be used to construct the predictive model for the clogging behavior of the PC.The combined algorithm has the advantage of higher reliability and validity than the random hyperparameters selection.For the verification process,the developed model was able to obtain values of 0.9469 and 1.8148 for the R and RMSE,showing that the developed machine learning model can accurately be used to evaluate and predict the clogging behavior of the PC,guiding the mix-design of PC from the perspective of durability.The size of the clogging sand is the most important parameter and the thickness of the sample is the least significant factor affecting the clogging behavior.The proportions of the smallest aggregate size and largest aggregate size are the two most important design parameters of concrete with the consideration of the relatively higher importance scores,showing these two aggregates should be given special attention in future PC design for anti-clogging purposes.
文摘Cyanobacterial harmful algal blooms are a major threat to freshwater eco-systems globally. To deal with this threat, researches into the cyanobacteria bloom in fresh water lakes and rivers have been carried out all over the world. This review presents an overlook of studies on cyanobacteria blooms. Conventional studies mainly focus on investigating the environmental factors influencing the blooms, with their limitation in lack of viewing the microbial community structures. Metagenomics study provides insight into the internal community structure of the cyanobacteria at the blooming, and there are researchers reported that sequence data was a better predictor than environmental factors. This further manifests the significance of the metagenomic study. However, large number of the latter appears to be confined only to present snapshoot of the microbial community diversity and structure. This type of investigation has been valuable and important, whilst an effort to integrate and coordinate the conventional approaches that largely focus on the environmental factors control, and the Metagenomics approaches that reveals the microbial community structure and diversity, implemented through machine learning techniques, for a holistic and more comprehensive insight into the cause and control of Cyanobacteria blooms, appear to be a trend and challenge of the study of this field.
文摘The singlet and triplet excited-state refraction cross-sections of dimethyl sulfoxide (DMSO) solutions of ten zinc phthalocyanine derivatives with mono-or tetra-peripheral substituents at 532 nm were obtained by simultaneous fitting of closed-aperture Z scans with both nanosecond and picosecond pulse widths. Self-focusing of both nanosecond and picosecond laser pulses was observed in all complexes at 532-nm wavelength. The complexes with tetra-substituents at the ?-position exhibit relatively larger refraction cross-sections than the other complexes. The wavelength dependence of the singlet refraction cross-section of a representative complex was observed to be non-monotonic in the range of 470 - 550 nm.
