The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical appr...The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.展开更多
For a soft rock tunnel under high stress in jointed and swell soft rock （HJS）, two construction schemes pilot-tunneling enlarging excavation and step-by-step excavation were optimized using FLAC20, and the deformati...For a soft rock tunnel under high stress in jointed and swell soft rock （HJS）, two construction schemes pilot-tunneling enlarging excavation and step-by-step excavation were optimized using FLAC20, and the deformation effects of the two construction schemes were verified by field tests. Based on engineer- ing geological investigation and mechanical analysis of large deformations, the complex deformation mechanisms of stress expansion and structural deformation of the soft rock tunnel were confirmed, and support countermeasures from the complex deformation mechanism converted to a single type were proposed, and the support parameters were optimized by field tests. These technologies were proved by engineering practice, which produced significant technical and economic benefits.展开更多
The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solutio...The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.展开更多
The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and ster...The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.展开更多
文摘The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.
基金financially supported by the National Natural Science Foundation of China (Nos. 51474188, 51074140 and 51310105020)the Natural Science Foundation of Hebei Province (No. E2014203012)the Program for Taihang Scholars
文摘For a soft rock tunnel under high stress in jointed and swell soft rock （HJS）, two construction schemes pilot-tunneling enlarging excavation and step-by-step excavation were optimized using FLAC20, and the deformation effects of the two construction schemes were verified by field tests. Based on engineer- ing geological investigation and mechanical analysis of large deformations, the complex deformation mechanisms of stress expansion and structural deformation of the soft rock tunnel were confirmed, and support countermeasures from the complex deformation mechanism converted to a single type were proposed, and the support parameters were optimized by field tests. These technologies were proved by engineering practice, which produced significant technical and economic benefits.
文摘The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
文摘The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed.An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced.Design of robust knowledge bases is performed using a developed computational intelligence-quantum/soft computing toolkit(QC/SCOptKBTM).The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described.The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described.The general design methodology of a generalizing control unit based on the physical laws of quantum computing(quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal)is considered.The modernization of the pattern recognition system based on stereo vision technology presented.The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system.The main objective of this article is to demonstrate the advantages of the approach based on quantum/soft computing.
