Evaluation of the Performance and Ductility Index of Concrete Structures Using Advanced Composite Material Strengthening Methods

The performance of concrete structures deteriorates over time. Thus, improving their performance using fiber-reinforced polymers (FRPs), PS strands, and various strengthening methods is important. Reinforced concrete (RC) and prestressed concrete (PSC) structures develop initial cracks in concrete during bending tests, and destruction occurs over a certain period of time after a certain load is generated, and then after the reinforcements and strands yield. However, in the case of FRP structures, after an initial concrete crack occurs, FRPs exhibit a rapid shape deformation of the structure after yielding. Thus, in this study we used FRP and PS strand materials and evaluated the ductility index using the load-displacement results obtained from structural tests conducted using various strengthening methods. The ductility index evaluation method compares and analyzes the change rates in the ductility index of PSC and RC structures based on a method that uses structural deflection and the derivation of the energy area ratio. The ductility evaluation results based on the energy area ratio at the crack, yield, and ultimate points showed that all the RC structures, except for the specimens strengthened with reinforcing materials from company H, were in the ductility and semi-ductility sections. Thus, all the PSC structures, except for the control specimens and PH4NP, were found to be brittle.

A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

Behaviour of Incorporation of Bacteria in Concrete

The life of the healing in concrete is many years old and when the study was done on this, outcomes were coinciding with the auto-genus healing process which was capable to heal the micro cracks inside cementitious based materials and hereafter research led to the study of autonomous healing. In the autonomous healing process, the main aim was to fill the cracks and heal the fracture at the macro level. This was possible with the help of bacteria that were embedded in the cementitious based material with different technologies and methods. In this paper, Enterobacter species and Cohnii bacteria were incorporated into concrete. The behaviour of bacterial concrete was investigated in terms of compressive, tensile, flexural strength and ultrasonic pulse velocity. It observed that the compressive strength of concrete was increased by 11.5%, flexural strength increased by 11.9%, tensile strength increased by 12.8% with the replacement of Cohnii bacteria as compare to conventional concrete. These bacteria have been proved a positive approach to the healing process in cementitious based material. Also, the important criterion has been studied which is essential when dealing with the autonomous healing process. Both the bacteria generate the calcite that helps to fill the concrete crack and voids if water come in to contact.