作者机构：
School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan;430023, China;The Third Engineering Scientific Research Institute of the Headquarters of the General Staff, Luoyang;471023, China;[Guo, Jian; Shen, Shuang-Shuang; Xia, Peng] School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan
通讯机构：
School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan, China
通讯机构：
[Gong, Jing] W;Wuhan Polytech Univ, Res Inst Architecture, Wuhan 430023, Peoples R China.
会议名称：
3rd International Conference on Civil Engineering, Architecture and Building Materials (CEABM 2013)
会议时间：
MAY 24-26, 2013
会议地点：
Jinan, PEOPLES R CHINA
会议主办单位：
[Gong, Jing;Guo, Jian] Wuhan Polytech Univ, Res Inst Architecture, Wuhan 430023, Peoples R China.^[Gong, Jing;Huang, Ming;Liu, Lisheng] Wuchang Univ Technol, Coll Urban Construct, Wuhan 430023, Peoples R China.
会议论文集名称：
Applied Mechanics and Materials
关键词：
Circular economy;Eco-industrial parks;Sustainable development
摘要：
The emerging of environment, resources, and other global problems make the traditional industrial system of sustainable development facing serious challenges. According to the industry condition of Wuhan district, the paper summarizes the practice of establishing modern eco-industrial parks in the character of cycle economy, and brings about the new idea for the construction and development of domestic industrial parks.
摘要：
Coal reservoir in Shanxi is influenced by sedimentary environment, diagenesis and structure. The reservoir is characterized by heterogeneity and nonlinearity distribution. An intelligence evaluation model, based on modified particle swarm optimization (MPSO) algorithm with Elman neural network (ENN), is constructed. The model is corresponding to reservoir physical property by the use of logging, core and test data, and it is employed to classify rock types of coal reservoir. The case results indicate that this model is feasible and effective in the rock classification, so it provides an effective method for identification of lithology and mineral rock classification.
会议名称：
Global Conference on Civil, Structural and Environmental Engineering / 3rd International Symp on Multi-field Coupling Theory of Rock and Soil Media and its Applications
会议时间：
OCT 20-21, 2012
会议地点：
China Three Gorges Univ, Yichang, PEOPLES R CHINA
摘要：
A wavelet estimation method is presented herein to estimate deep pit settlement. In this method, the pit settlement is decomposed into the trend settlement and the stochastic settlement by using Wavelet Analysis based on the characteristic of influencing factor. The model identifier is established by using artificial neural network (ANN), and trained to approximate the trend settlement. Then, the prediction controller developed could be applied for estimating the actual settlement. Finally, the verification examples show that the WIAN is an effective tool for predicting the pit settlement dynamically , high precision could be expected and achieved.
作者机构：
[Jian Guo] School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan, China;[Fei Tan] Department of Controlled Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
通讯机构：
School of Civil Engineering and Architecture, Wuhan Polytechnic University, China
会议名称：
2010 International Conference on Electrical and Control Engineering
会议时间：
June 2010
会议地点：
Wuhan, China
会议论文集名称：
2010 International Conference on Electrical and Control Engineering
作者机构：
[郭健; 龚静] College of Civil Engineering, Wuhan Polytechnic University, Wuhan 430023, China;[余飞] Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
通讯机构：
College of Civil Engineering, Wuhan Polytechnic University, China
会议名称：
The 4th Internatiuonal Symposium on Lifetime Engineering of Civil Infrastructure(第四届土木工程结构生命周期国际学术研讨会)
会议时间：
2009-10-26
会议地点：
长沙
会议论文集名称：
The 4th Internatiuonal Symposium on Lifetime Engineering of Civil Infrastructure(第四届土木工程结构生命周期国际学术研讨会)论文集
关键词：
Subway foundation pit;self-adaptive PSO;radial basis function;prediction analysis
摘要：
Particle swarm optimization (PSO) was improved to ovecome the shortcomings of PSO, and a self-adaptive particle swarm optimization (SAPSO) was proposed in paper. The SAPSO was combined with radial basis function neural network (RBF-NN) to form a SAPSO-NN algorithm. A SAPSO-NN prediction system was established for the deformation prediction of subway foundation pit. The results of an engineering case indicate that the intelligent prediction system is efficientive in the complex underground structures.
