Artificial Neural Network and Nonlinear Models for Gelling Time and Maximum Curing Temperature Rise in Polymer Grouts-Reference-Cited by-同舟云学术

Artificial Neural Network and Nonlinear Models for Gelling Time and Maximum Curing Temperature Rise in Polymer Grouts

Published:2011-04 Issue:4 Volume:23 Page:372-377
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Demircan Emrah¹²³,Harendra Sivaram¹²³,Vipulanandan Cumaraswamy¹²³

Affiliation:

1. MSCE Student, Dept. of Civil and Environmental Engineering, Univ. of Houston, Houston, TX 77204-4003.

2. Ph.D. Student, Dept. of Civil and Environmental Engineering, Univ. of Houston, Houston, TX 77204-4003.

3. Professor and Director, Center for Innovative Grouting Material and Technology (CIGMAT) and Texas Hurricane Center for Innovative Technology (THC-IT), Dept. of Civil and Environmental Engineering, Univ. of Houston, Houston, TX 77204-4003 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29MT.1943-5533.0000172

Reference12 articles.

1. Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks

2. ASTM. (2007). “Standard practice for rehabilitation of sewers using chemical grouting.” ASTM F 2304-03 West Conshohocken PA.

3. Experimental Study of Horizontal Barrier Formation by Colloidal Silica

4. Stabilization of Liquefiable Soils Using Colloidal Silica Grout

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Investigation of Hybrid Intelligence Models to Optimize Cement Kiln Content Based on the Failure Stress, Chemical Composition, and Loss on Ignition of Mortar;Journal of Materials in Civil Engineering;2024-03

2. Smart Predictive Modeling for Compressive Strength in Sisal-Fiber-Reinforced-Concrete Composites: Harnessing SVM, GP, and ANN Techniques;Multiscale Science and Engineering;2024-03

3. Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques;Computer Modeling in Engineering & Sciences;2024

4. Modelling the compressive strength of high-performance concrete containing metakaolin using distinctive statistical techniques;Results in Control and Optimization;2023-09

5. Machine learning approach for predicting concrete compressive, splitting tensile, and flexural strength with waste foundry sand;Journal of Building Engineering;2023-07