摘要：
In order to find out the slaking mechanism of carbonaceous mudstone, this study puts two different samples in the indoor soaking slaking test, one of dried sample and the other original one. After comparing the characteristics of carbonaceous mudstone slaking particles's distribution under different moisture content conditions, the result shows: The main reason for carbonaceous mudstone slaking is the change of moisture content resulting from dry-wet circulation. The difference in moisture content partly affects the process and slaking product before slaking. The lower the moisture content is, the smaller the slaking particle diameter is and the more thoroughly the slaking is. As the particle diameter of the slaking product decreases, its slaking gradually weakens and even disappears.
期刊：
Geotechnical Special Publication,2011年(220 GSP):90-99 ISSN：0895-0563
通讯作者：
Fu, H.(fuhy1@163.com)
作者机构：
[Fu, Hongyuan; Lou, Xiaoyu] School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Hunan, Changsha, 410076, China
通讯机构：
[Fu, H.] S;School of Traffic and Transportation Engineering, Changsha University of Science and Technology, China
会议名称：
GeoHunan International Conference 2011
会议时间：
June 9-11, 2011
会议地点：
Hunan, China
会议论文集名称：
Advances in Pile Foundations, Geosynthetics, Geoinvestigations, and Foundation Failure Analysis and Repairs
作者机构：
[Fu, Hong-Yuan] School of Communication and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China;Hunan Huagang Communications Planning and Design Research Institute, Changsha 410076, China
通讯机构：
School of Communication and Transportation Engineering, Changsha University of Science and Technology, China
作者：
Zhang, Li;Liu, Jian Hua;Fu, Hong Yuan;Guo, Zhu
期刊：
Geotechnical Special Publication,2011年(217 GSP):188-194 ISSN：0895-0563
通讯作者：
Zhang, L.
作者机构：
[Zhang, Li] Hunan Huagang Communications Planning and Design Research Institute, Changsha;Hunan, 410004, China;[Liu, Jian Hua; Fu, Hong Yuan] School of Communication and Transportation Engineering, Changsha University of Science and Technology, Changsha;[Guo, Zhu] Hunan Supporting Team for the Reconstruction of Lixian, Changsha;Hunan, 410010, China
通讯机构：
[Zhang, L.] H;Hunan Huagang Communications Planning and Design Research Institute, Changsha
会议名称：
2011 GeoHunan International Conference - Advances in Unsaturated Soil, Geo-Hazard, and Geo-Environmental Engineering
会议时间：
9 June 2011 through 11 June 2011
会议论文集名称：
Advances in Unsaturated Soil, Geo-Hazard, and Geo-Environmental Engineering
作者机构：
[蒋中明; Zeng, Ling; 付宏渊] School of Civil Engineering and Architecture, Changsha University of Science and Technology, Changsha 410004, China;[李怀玉] School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China
通讯机构：
School of Civil Engineering and Architecture, Changsha University of Science and Technology, China
期刊：
Geotechnical Special Publication,2009年(196):85-92 ISSN：0895-0563
通讯作者：
Fu, H.(fuhy1@163.com)
作者机构：
[Fu, Hongyuan; Zhang, Yang; Kuang, Bo] School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Hunan Changsha, 410076, China
通讯机构：
[Fu, H.] S;School of Traffic and Transportation Engineering, Changsha University of Science and Technology, China
会议名称：
2009 GeoHunan International Conference - New Technologies in Construction and Rehabilitation of Portland Cement Concrete Pavement and Bridge Deck Pavement
会议时间：
August 3, 2009 - August 6, 2009
会议地点：
Changsha, Hunan, China
会议论文集名称：
New Technologies in Construction and Rehabilitation of Portland Cement Concrete Pavement and Bridge Deck Pavement
摘要：
This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection, we present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks have advantages of both fuzzy expert systems (knowledge representation) and artificial neural networks (learning). The fuzzy neural networks can be trained successfully to estimate NVWQ for different traffic flow patterns and different conditions intersection. This greatly reduces a lot of effort of extracting traffic expert's knowledge into fuzzy if-then rules. All we have to do is to present training data to the network which will figure out its own rules through internal representation. In traffic signal control system, detection of traffic variables at intersection, such as NVWQ is very important and is the basic input data to determine signal timing.
