Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Behnam Kiani.pdf (1.52 MB)

ETD Abstract Container

Abstract Header

NOVEL REPAIR MATERIAL SELECTION METHODOLOGY FOR CONCRETE STRUCTURES AND RELATED LONG - TERM PERFORMANCE PREDICTION MODEL

Kiani, Behnam
http://rave.ohiolink.edu/etdc/view?acc_num=akron1490976953391154

Abstract Details

2017, Doctor of Philosophy, University of Akron, Civil Engineering.
The main objective of this thesis is to increase the performance of repair materials in concrete overlays. To meet this objective, a new material selection process is applied. Then, with a focus on volume change in concrete overlays new models for shrinkage and creep are developed, and the ACI model for shrinkage is modi ed. First, a straightforward repair material selection procedure which covers all criteria and technical requirements is developed. In this regard, a recently proposed MCDM method, namely comprehensive VIKOR is used. Then, the strategy is applied on ve di erent types of patch repair materials to validate the accuracy of the outcomes of the proposed procedure. Second, the improvement of ACI model for shrinkage of concrete containing three types of pozzolans including silica fume (SF) Fly ash (FA) and Slag (SL) is conducted base on a comprehensive database. Particle Swarm Optimization (PSO) method is used to modify time function of ACI model for each type of pozzolan. In addition, a new correction factor associated with compressive strength is generated to capture the e ect of dosage and type of each pozzolans. The results of several indicators iii show better prediction performance the modi ed ACI model compared to it's original formula. Third, new empirical models are derived to predict the compressive strength of preformed foam cellular concrete using volumetric and weighted approaches. The proposed models are generated by utilizing a robust predictive tool known as genetic programming. A comprehensive database is collected from the literature to cover a wide range of mixture components (such as sand and pozzolans) and mix proportions. The models link the compressive strength to binder, water, and foam volume. Validation of the best model is carried out by using a portion of the data set that is not employed in the calibration process. A comparative study is conducted to evaluate the performance of the proposed model versus other models presented in the literature. Sensitivity and parametric analyses were conducted. The nal model has a simple formulation and provides better prediction performance than the other models in the literature.
Robert Y Liang (Advisor)
Alper Buldum (Committee Member)
Junliang Tao (Committee Member)
Zhe Luo (Committee Member)
Chang Ye (Committee Member)
120 p.
Civil Engineering
Concrete; shrinkage; repair; ACI model; Preformed-Foam Cellular Concrete; PSO; VIKOR method; Prediction models; Optimization;

Recommended Citations

Citations

  • Kiani, B. (2017). NOVEL REPAIR MATERIAL SELECTION METHODOLOGY FOR CONCRETE STRUCTURES AND RELATED LONG - TERM PERFORMANCE PREDICTION MODEL [Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1490976953391154

    APA Style (7th edition)

  • Kiani, Behnam. NOVEL REPAIR MATERIAL SELECTION METHODOLOGY FOR CONCRETE STRUCTURES AND RELATED LONG - TERM PERFORMANCE PREDICTION MODEL. 2017. University of Akron, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1490976953391154.

    MLA Style (8th edition)

  • Kiani, Behnam. "NOVEL REPAIR MATERIAL SELECTION METHODOLOGY FOR CONCRETE STRUCTURES AND RELATED LONG - TERM PERFORMANCE PREDICTION MODEL." Doctoral dissertation, University of Akron, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1490976953391154

    Chicago Manual of Style (17th edition)

akron1490976953391154
424
© 2017, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.