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浏览/检索结果: 共3条，第1-3条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择发表日期升序发表日期降序作者升序作者降序提交时间升序提交时间降序题名升序题名降序 Nondestructive assessment of the compressive strength of concrete with high volume slag 期刊论文 Materials Characterization, 2020, 卷号: 162 作者:  Yang, Yonggan;  Zhan, Binggen;  Wang, Jingfeng;  Zhang, Yunsheng 收藏  |  浏览/下载：6/0  |  提交时间：2020/11/14 Aggregates  Compressive strength  Computerized tomography  Concrete aggregates  Particle size  Polymethyl methacrylates  Pore structure  Slags  X rays  Coarse aggregates  Compressive strength of concrete  Mercury intrusion porosimetry  Nondestructive assessment  Rebound value  Strength curves  X-ray computed tomography  X-ray CT   Correlation of UCS Rating with Schmidt Hammer Surface Hardness for Rock Mass Classification 期刊论文 Rock Mechanics and Rock Engineering, 2017, 卷号: 50, 期号: 1, 页码: 195-203 作者:  Wang, Hu;  Lin, Hang*;  Cao, Ping 收藏  |  浏览/下载：7/0  |  提交时间：2019/12/03 Rebound value  Rock mass classification  Schmidt hammer  Surface hardness  Uniaxial compressive strength   Prediction of concrete strength using fuzzy neural networks 会议论文 Haikou, China, June 18, 2011 - June 20, 2011 作者:  Xu, Jing;  Wang, Xiuli 收藏  |  浏览/下载：7/0  |  提交时间：2020/11/15 Algebra  Building materials  Civil engineering  Compressive strength  Construction equipment  Forecasting  Fuzzy inference  Fuzzy logic  Fuzzy systems  Learning algorithms  Least squares approximations  Mathematical models  Network architecture  Adaptive neuro-fuzzy inference system  Automatic-learning  Average relative error  Compressive strength of concrete  Concrete strength  Concrete strength prediction  Condition parameters  Expert experience  Fuzzy logic inference  Fuzzy Neural Networks (FNN)  Gradient Descent method  Hybrid-learning algorithm  Input and outputs  Input-output data  Intelligent prediction  Least squares methods  Parameter set  Power functions  Practical engineering  Prediction model  Rebound value  Relative standard error  Specific equations  Strength values  Takagi-sugeno  Test results  Training patterns