人类进入的21世纪,是一个人类真正需要进行生态反思的世纪。反思我们与自然的关系,反思我们与地球生命支持系统中植物、动物、抑或微生物的关系,反思我们与地球环境保障系统中的江河湖海、山川大地、森林草原、城镇乡村的关系。地球生...人类进入的21世纪,是一个人类真正需要进行生态反思的世纪。反思我们与自然的关系,反思我们与地球生命支持系统中植物、动物、抑或微生物的关系,反思我们与地球环境保障系统中的江河湖海、山川大地、森林草原、城镇乡村的关系。地球生物圈尚存的完整自然生态系统愈来愈少,人类未来生存、发展及适应全球变化的珍贵缓冲区(buffers)正快速萎缩,地球表面随处可见的3D系统(degraded,damaged and destroyed ecosystems)正快速增加,人类生命支撑系统中最为重要的生物多样性也正以前所未有的速度丧失,人类生存与发展之基失稳,亟待从生态保护理念出发,探索生态技术解决方案。在辨析生态、生态保护与生态修复内涵的基础上,基于文献计量学方法,以生态保护(ecological protection)和生态修复(ecological restoration)为主题词在Web of Science上检索了生态保护与生态修复近70年发文量及国际主流杂志发文量,分析了生态系统退化机制及驱动力,总结了国外生态保护与生态修复所依托的先进理论和技术方法,以期为我国生态系统保护与退化生态系统修复提供一定的理论指导。展开更多
Based on machine-learning(ML) and analytical methods, a hybrid method is developed herein to predict the ground-displacement field(GDF) caused by tunneling. The extreme learning machine(ELM), as a single hidden layer ...Based on machine-learning(ML) and analytical methods, a hybrid method is developed herein to predict the ground-displacement field(GDF) caused by tunneling. The extreme learning machine(ELM), as a single hidden layer feedforward neural network, is used as an ML model to predict maximum settlement smaxof the ground surface. The particle swarm optimization(PSO) algorithm is applied to optimize the parameters for the ELM method, namely, weight and bias values from the input layer to the hidden layer. The mean square error of the k-fold cross validation sets is considered the fitness function of the PSO algorithm. For 38 data samples from published papers, 30 samples are used as the training set, and 8 samples are used as the test set. For the test samples, the error of five samples ranges between-5 and 5 mm. The error of only one sample is slightly greater than 10 mm. The proposed PSO-ELM method demonstrates good prediction performance of smax. A deformation parameter of the nonuniform displacement mode for the tunnel cross-section is calibrated based on predicted smax. When the determined nonuniform displacement mode is used as the boundary condition of the tunnel cross-section, the GDF of a shallow circular tunnel is analytically predicted based on the complex-variable method prior to tunnel excavation. For a specific engineering case,i.e., the Heathrow Express tunnel, the proposed PSO-ELM-analytical method can well predict the surface-settlement trough curve, horizontal displacements at different depths, and vertical displacements above the tunnel.展开更多
文摘人类进入的21世纪,是一个人类真正需要进行生态反思的世纪。反思我们与自然的关系,反思我们与地球生命支持系统中植物、动物、抑或微生物的关系,反思我们与地球环境保障系统中的江河湖海、山川大地、森林草原、城镇乡村的关系。地球生物圈尚存的完整自然生态系统愈来愈少,人类未来生存、发展及适应全球变化的珍贵缓冲区(buffers)正快速萎缩,地球表面随处可见的3D系统(degraded,damaged and destroyed ecosystems)正快速增加,人类生命支撑系统中最为重要的生物多样性也正以前所未有的速度丧失,人类生存与发展之基失稳,亟待从生态保护理念出发,探索生态技术解决方案。在辨析生态、生态保护与生态修复内涵的基础上,基于文献计量学方法,以生态保护(ecological protection)和生态修复(ecological restoration)为主题词在Web of Science上检索了生态保护与生态修复近70年发文量及国际主流杂志发文量,分析了生态系统退化机制及驱动力,总结了国外生态保护与生态修复所依托的先进理论和技术方法,以期为我国生态系统保护与退化生态系统修复提供一定的理论指导。
基金supported by the National Natural Science Foundation of China (Grant No. 52025084)。
文摘Based on machine-learning(ML) and analytical methods, a hybrid method is developed herein to predict the ground-displacement field(GDF) caused by tunneling. The extreme learning machine(ELM), as a single hidden layer feedforward neural network, is used as an ML model to predict maximum settlement smaxof the ground surface. The particle swarm optimization(PSO) algorithm is applied to optimize the parameters for the ELM method, namely, weight and bias values from the input layer to the hidden layer. The mean square error of the k-fold cross validation sets is considered the fitness function of the PSO algorithm. For 38 data samples from published papers, 30 samples are used as the training set, and 8 samples are used as the test set. For the test samples, the error of five samples ranges between-5 and 5 mm. The error of only one sample is slightly greater than 10 mm. The proposed PSO-ELM method demonstrates good prediction performance of smax. A deformation parameter of the nonuniform displacement mode for the tunnel cross-section is calibrated based on predicted smax. When the determined nonuniform displacement mode is used as the boundary condition of the tunnel cross-section, the GDF of a shallow circular tunnel is analytically predicted based on the complex-variable method prior to tunnel excavation. For a specific engineering case,i.e., the Heathrow Express tunnel, the proposed PSO-ELM-analytical method can well predict the surface-settlement trough curve, horizontal displacements at different depths, and vertical displacements above the tunnel.
