Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete-Reference-Cited by-同舟云学术

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Published:2016-11 Issue:6 Volume:30 Page:04016029
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Omran Behzad Abounia¹,Chen Qian²,Jin Ruoyu³

Affiliation:

1. Ph.D. Candidate, Construction Systems Management, Dept. of Food, Agricultural, and Biological Engineering, Ohio State Univ., 590 Woody Hayes Dr., Columbus, OH 43210.

2. Associate Professor, Construction Systems Management, Dept. of Food, Agricultural, and Biological Engineering, Ohio State Univ., 590 Woody Hayes Dr., Columbus, OH 43210 (corresponding author).

3. Assistant Professor, Dept. of Architecture and Built Environment, Univ. of Nottingham Ningbo China, 323 Science and Engineering Building, 199 Taikang East Rd., Ningbo 315100, China; formerly, Graduate Research Assistant, Ohio State Univ., 590 Woody Hayes Dr., Columbus, OH 43210.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000596

Reference64 articles.

1. Experimental Investigation and Development of Artificial Neural Network Model for the Properties of Locally Produced Light Weight Aggregate Concrete

2. Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine

3. Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete

4. Multicollinearity

5. Neural networks for predicting compressive strength of structural light weight concrete

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