Bond Performance between Corroded Steel and Recycled Aggregate Concrete Incorporating Nano Silica

The current research project mainly aims to investigate the corrosion resistance and bond performance of steel reinforced recycled aggregate concrete incorporating nano-silica under both normal and corrosive environmental conditions. The experimental part includes testing of 180 pull-out specimens prepared from 12 different mixtures. The main parameters studied were the amount of recycled aggregate (RCA) (i.e. 0%, 25%, 50% and 100%), nano silica (1.5% and 3%), steel embedment length as well as steel bar diameter (12 and 20mm). Different levels of corrosion were electrochemically induced by applying impressed voltage technique for 2, 5, 10 and 15 days. The experimental observations mainly focused on the corrosion level in addition to the ultimate bond, failure modes and slips occurred. Experimental results showed that the bond performance between un-corroded steel and recycled aggregate concrete slightly reduced, while a significant degradation was observed after being exposed to corrosive conditions, in comparison to normal concrete. On the other hand, the use of nano silica (NS) showed a reasonable bond enhancement with both normal and RCA concretes under normal conditions. However, much better influence in terms of bond and corrosion resistance was observed under advancing levels of corrosion exposure, reflecting the improvement in corrosion resistance. Therefore, NS was superbly effective in recovering the poor performance in bond for RCA concretes. More efficiency was reported with RCA concretes compared to the conventional concrete. The bond resistance slightly with a small amount of corrosion (almost 2% weight loss), then a significant bond degradation occurs with further corrosion. The influence of specific surface area and amount of nano silica on the performance of concrete with different water/binder (w/b) ratios has been also studied, using 63 different mixtures produced with three different types of colloidal NS having various surface areas and particle sizes. The results showed that the performance of concrete is heavily influenced by changing the surface area of nano silica. Amongst the three used types of nano silica, NS with SSA of 250 m2 /g achieved the highest enhancement rate in terms of compressive strength, water absorption and microstructure analysis, followed by NS with SSA of 500 m2/g, whilst NS with SSA of 51.4 m2 /g was less advantageous for all mixtures. The optimum nano silica ratio in concrete is affected by its particle size as well as water to binder ratio. The feasibility of the impact-echo method for identifying the corrosion was evaluated and compared to the corrosion obtained by mass loss method. The results showed that the impact-echo testing can be effectively used to qualitatively detect the damage caused by corrosion in reinforced concrete structures. A significant difference in the dominant frequencies response was observed after exposure to the high and moderate levels of corrosion, whilst no clear trend was observed at the initial stage of corrosion. Artificial neural network models were also developed to predict bond strength for corroded/uncorroded steel bars in concrete using the main influencing parameters (i.e., concrete strength, concrete cover, bar diameter, embedment length and corrosion rate). The developed models were able to predict the bond strength with a high level of accuracy, which was confirmed by conducting a parametric study.

Grantor dc:publisher.institution

University of Bradford

Year dc:date.issued

2020

Author dc:creator

• Alhawat, Musab M.

Advisor dc:contributor.advisor

• Ashour, Ashraf

• Bond strength
• Pull-out test
• Mass loss method
• Reinforcement corrosion
• Nano silica
• Recycled aggregate
• Specific surface area
• Impact-echo method
• Artificial Neural Network
• Steel reinforced concrete

Statement dc:rights

• <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10454/18430

OAI identifier oai:identifier

oai:bradscholars.brad.ac.uk:10454/18430